{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习工程师纳米学位\n",
    "## 模型评价与验证\n",
    "## 项目 1: 预测波士顿房价\n",
    "\n",
    "\n",
    "欢迎来到机器学习工程师纳米学位的第一个项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能来让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以**编程练习**开始的标题表示接下来的内容中有需要你必须实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以**TODO**标出。请仔细阅读所有的提示！\n",
    "\n",
    "除了实现代码外，你还**必须**回答一些与项目和实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。你的项目将会根据你对问题的回答和撰写代码所实现的功能来进行评分。\n",
    "\n",
    ">**提示：**Code 和 Markdown 区域可通过 **Shift + Enter** 快捷键运行。此外，Markdown可以通过双击进入编辑模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第一步. 导入数据\n",
    "在这个项目中，你将利用马萨诸塞州波士顿郊区的房屋信息数据训练和测试一个模型，并对模型的性能和预测能力进行测试。通过该数据训练后的好的模型可以被用来对房屋做特定预测---尤其是对房屋的价值。对于房地产经纪等人的日常工作来说，这样的预测模型被证明非常有价值。\n",
    "\n",
    "此项目的数据集来自[UCI机器学习知识库(数据集已下线)](https://archive.ics.uci.edu/ml/datasets.html)。波士顿房屋这些数据于1978年开始统计，共506个数据点，涵盖了麻省波士顿不同郊区房屋14种特征的信息。本项目对原始数据集做了以下处理：\n",
    "- 有16个`'MEDV'` 值为50.0的数据点被移除。 这很可能是由于这些数据点包含**遗失**或**看不到的值**。\n",
    "- 有1个数据点的 `'RM'` 值为8.78. 这是一个异常值，已经被移除。\n",
    "- 对于本项目，房屋的`'RM'`， `'LSTAT'`，`'PTRATIO'`以及`'MEDV'`特征是必要的，其余不相关特征已经被移除。\n",
    "- `'MEDV'`特征的值已经过必要的数学转换，可以反映35年来市场的通货膨胀效应。\n",
    "\n",
    "运行下面区域的代码以载入波士顿房屋数据集，以及一些此项目所需的Python库。如果成功返回数据集的大小，表示数据集已载入成功。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 载入此项目所需要的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import visuals as vs # Supplementary code\n",
    "\n",
    "# 检查你的Python版本\n",
    "from sys import version_info\n",
    "# 切换成pythho\n",
    "# if version_info.major != 2 and version_info.minor != 7:\n",
    "#     raise Exception('请使用Python 2.7来完成此项目')\n",
    "if version_info.major != 3 and version_info.minor != 5:\n",
    "    raise Exception ('请使用Python 3.5 来完成此项目')\n",
    "# 让结果在notebook中显示\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston housing dataset has 489 data points with 4 variables each.\n"
     ]
    }
   ],
   "source": [
    "# 载入波士顿房屋的数据集\n",
    "data = pd.read_csv('housing.csv')\n",
    "prices = data['MEDV']\n",
    "features = data.drop('MEDV', axis = 1)\n",
    "    \n",
    "# 完成\n",
    "print (\"Boston housing dataset has {} data points with {} variables each.\".format(*data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第二步. 分析数据\n",
    "在项目的第一个部分，你会对波士顿房地产数据进行初步的观察并给出你的分析。通过对数据的探索来熟悉数据可以让你更好地理解和解释你的结果。\n",
    "\n",
    "由于这个项目的最终目标是建立一个预测房屋价值的模型，我们需要将数据集分为**特征(features)**和**目标变量(target variable)**。\n",
    "- **特征** `'RM'`， `'LSTAT'`，和 `'PTRATIO'`，给我们提供了每个数据点的数量相关的信息。\n",
    "- **目标变量**：` 'MEDV'`，是我们希望预测的变量。\n",
    "\n",
    "他们分别被存在`features`和`prices`两个变量名中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 1：基础统计运算\n",
    "你的第一个编程练习是计算有关波士顿房价的描述统计数据。我们已为你导入了` numpy `，你需要使用这个库来执行必要的计算。这些统计数据对于分析模型的预测结果非常重要的。\n",
    "在下面的代码中，你要做的是：\n",
    "- 计算`prices`中的`'MEDV'`的最小值、最大值、均值、中值和标准差；\n",
    "- 将运算结果储存在相应的变量中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for Boston housing dataset:\n",
      "\n",
      "Minimum price: $105,000.00\n",
      "Maximum price: $1,024,800.00\n",
      "Mean price: $454,342.94\n",
      "Median price $438,900.00\n",
      "Standard deviation of prices: $165,171.13\n"
     ]
    }
   ],
   "source": [
    "#TODO 1\n",
    "\n",
    "#目标：计算价值的最小值\n",
    "minimum_price = np.min(prices)\n",
    "\n",
    "#目标：计算价值的最大值\n",
    "maximum_price = np.max(prices)\n",
    "\n",
    "#目标：计算价值的平均值\n",
    "mean_price = np.mean(prices)\n",
    "\n",
    "#目标：计算价值的中值\n",
    "median_price = np.median(prices)\n",
    "\n",
    "#目标：计算价值的标准差\n",
    "std_price = np.std(prices)\n",
    "\n",
    "#目标：输出计算的结果\n",
    "print (\"Statistics for Boston housing dataset:\\n\")\n",
    "print (\"Minimum price: ${:,.2f}\".format(minimum_price))\n",
    "print (\"Maximum price: ${:,.2f}\".format(maximum_price))\n",
    "print (\"Mean price: ${:,.2f}\".format(mean_price))\n",
    "print (\"Median price ${:,.2f}\".format(median_price))\n",
    "print (\"Standard deviation of prices: ${:,.2f}\".format(std_price))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1 - 特征观察\n",
    "\n",
    "如前文所述，本项目中我们关注的是其中三个值:`'RM'`、`'LSTAT'` 和`'PTRATIO'`，对每一个数据点:\n",
    "- `'RM'` 是该地区中每个房屋的平均房间数量；\n",
    "- `'LSTAT'` 是指该地区有多少百分比的业主属于是低收入阶层（有工作但收入微薄）；\n",
    "- `'PTRATIO'` 是该地区的中学和小学里，学生和老师的数目比（`学生/老师`）。\n",
    "\n",
    "_凭直觉，上述三个特征中对每一个来说，你认为增大该特征的数值，`'MEDV'`的值会是**增大**还是**减小**呢？每一个答案都需要你给出理由。_\n",
    "\n",
    "**提示：**你预期一个`'RM'` 值是6的房屋跟`'RM'` 值是7的房屋相比，价值更高还是更低呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x106743320>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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8x2GdreGae9LxnWEO1FzywmGR9/bRgUfNJS/qWzi1YDRtpFs+/jfILrbXslgJ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59v06LLytRLMPxy+02mPAqssBlfHka+/Xoa4hYmBTyfcJIETnD8QljU2FVfGMiBKCHh48b10j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\nSiLL8Lw1jdR/dYSp9PuDOs7FZPWmTzjpjnXv8NDS4WPOyi1mGxf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WiA0w/i5z2mg63mYG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va50zadTrBbVc5lG2qTE/iqKikOeDnSEGlXSzrk8zC0JLMkadK2Kp+i2wj4NCIxyZ3PbGPk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\nQqFQKHKEcsYVCoVCoVAoFIoc8f8BroIeOyFmaH4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1063b6518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CD0WeM/qYZxuOn51xDpE54RzGxGSNK3fsib1PtGZao6S05H604T+sYOO2+/lR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\niS16mKbVpIhmH9fs76Od319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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114a5b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QZ1faTsaW8ROieGSESiLuBt9NN9WQVuFAnOdVNYFBXl1p26VM4yfEckdGqEI0e0jNla7b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\nEKI0ZISEEEKUhoyQEEKI0vj/rQ8LTRZAPdwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11519b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ffRanltCC/c7JPYnrYHFrTOcHQg0lscdrseOWL10Q/Nz2uKdyA7/jwbCLdhp3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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153f4080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(data['RM'],data[\"MEDV\"],'o')\n",
    "plt.xlabel('RM')\n",
    "plt.ylabel('MEDV')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(data['LSTAT'],data['MEDV'],'o')\n",
    "plt.xlabel('LSTAT')\n",
    "plt.ylabel('MEDV')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(data['PTRATIO'],data['MEDV'],'o')\n",
    "plt.xlabel(\"PTRATIO\")\n",
    "plt.ylabel(\"MEDA\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1 - 回答：\n",
    "> 根据上图可以看出三个不同特征与目标变量的不同关系：\n",
    "\n",
    "> RM MEDA 彼此之间正相关,RM越大MEDA的值越大\n",
    "\n",
    "> LSTAT 与 MEDA 彼此之间属于负相关,LSTAT越大MEDA的值越小\n",
    "\n",
    "> PTRATIO 与 MEDA 彼此之间的关系不确定"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 2: 数据分割与重排\n",
    "接下来，你需要把波士顿房屋数据集分成训练和测试两个子集。通常在这个过程中，数据也会被重排列，以消除数据集中由于顺序而产生的偏差。\n",
    "在下面的代码中，你需要\n",
    "\n",
    "使用 `sklearn.model_selection` 中的 `train_test_split`， 将`features`和`prices`的数据都分成用于训练的数据子集和用于测试的数据子集。\n",
    "  - 分割比例为：80%的数据用于训练，20%用于测试；\n",
    "  - 选定一个数值以设定 `train_test_split` 中的 `random_state` ，这会确保结果的一致性；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 2\n",
    "\n",
    "# 提示： 导入train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features,prices,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2 - 训练及测试\n",
    "*将数据集按一定比例分为训练用的数据集和测试用的数据集对学习算法有什么好处？*\n",
    "\n",
    "*如果用模型已经见过的数据，例如部分训练集数据进行测试，又有什么坏处？*\n",
    "\n",
    "**提示：** 如果没有数据来对模型进行测试，会出现什么问题？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2 - 回答:\n",
    "> * 首先用训练集数据进行模型训练，保证模型的创建，然后再用测试集数据进行测试，查看模型是否过拟合，保证模型的准确率。\n",
    "> * 出现的问题，模型通过数据不断拟合，掩盖了模型的缺点，导致了再预测新的数据时候很有可能准确率降低\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第三步. 模型衡量标准\n",
    "在项目的第三步中，你需要了解必要的工具和技巧来让你的模型进行预测。用这些工具和技巧对每一个模型的表现做精确的衡量可以极大地增强你预测的信心。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习3：定义衡量标准\n",
    "如果不能对模型的训练和测试的表现进行量化地评估，我们就很难衡量模型的好坏。通常我们会定义一些衡量标准，这些标准可以通过对某些误差或者拟合程度的计算来得到。在这个项目中，你将通过运算[*决定系数*](http://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination) R<sup>2</sup> 来量化模型的表现。模型的决定系数是回归分析中十分常用的统计信息，经常被当作衡量模型预测能力好坏的标准。\n",
    "\n",
    "R<sup>2</sup>的数值范围从0至1，表示**目标变量**的预测值和实际值之间的相关程度平方的百分比。一个模型的R<sup>2</sup> 值为0还不如直接用**平均值**来预测效果好；而一个R<sup>2</sup> 值为1的模型则可以对目标变量进行完美的预测。从0至1之间的数值，则表示该模型中目标变量中有百分之多少能够用**特征**来解释。_模型也可能出现负值的R<sup>2</sup>，这种情况下模型所做预测有时会比直接计算目标变量的平均值差很多。_\n",
    "\n",
    "在下方代码的 `performance_metric` 函数中，你要实现：\n",
    "- 使用 `sklearn.metrics` 中的 [`r2_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) 来计算 `y_true` 和 `y_predict`的R<sup>2</sup>值，作为对其表现的评判。\n",
    "- 将他们的表现评分储存到`score`变量中。\n",
    "\n",
    "或 \n",
    "\n",
    "- (可选) 不使用任何外部库，参考[决定系数的定义](https://en.wikipedia.org/wiki/Coefficient_of_determination)进行计算，这也可以帮助你更好的理解决定系数在什么情况下等于0或等于1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 3\n",
    "\n",
    "# 提示： 导入r2_score\n",
    "\n",
    "def performance_metric(y_true, y_predict):\n",
    "    \"\"\"计算并返回预测值相比于预测值的分数\"\"\"\n",
    "    from sklearn.metrics import r2_score\n",
    "    score = r2_score(y_true,y_predict)\n",
    "\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 3 可选\n",
    "\n",
    "# 不允许导入任何计算决定系数的库\n",
    "\n",
    "def performance_metric2(y_true, y_predict):\n",
    "    \"\"\"计算并返回预测值相比于预测值的分数\"\"\"\n",
    "    import numpy as np\n",
    "    \n",
    "    arr_true = np.array(y_true)\n",
    "    y_mean = np.mean(y_true)\n",
    "#     回归平方和\n",
    "    ssreg = 0 \n",
    "#     残差平方和\n",
    "    ssres = 0\n",
    "#     总平方和\n",
    "    sstot = 0\n",
    "    \n",
    "    for i in y_predict:\n",
    "        ssreg += (i - y_mean)**2\n",
    "        \n",
    "    for index,item in enumerate(y_true):\n",
    "        ssres += (item - y_predict[index])**2\n",
    "    for i in y_true:\n",
    "        sstot += (i-y_mean)**2\n",
    "    \n",
    "    \n",
    "    score = 1 - (ssres/sstot)\n",
    "\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3 - 拟合程度\n",
    "\n",
    "假设一个数据集有五个数据且一个模型做出下列目标变量的预测：\n",
    "\n",
    "| 真实数值 | 预测数值 |\n",
    "| :-------------: | :--------: |\n",
    "| 3.0 | 2.5 |\n",
    "| -0.5 | 0.0 |\n",
    "| 2.0 | 2.1 |\n",
    "| 7.0 | 7.8 |\n",
    "| 4.2 | 5.3 |\n",
    "*你觉得这个模型已成功地描述了目标变量的变化吗？如果成功，请解释为什么，如果没有，也请给出原因。*  \n",
    "\n",
    "**提示**：运行下方的代码，使用`performance_metric`函数来计算模型的决定系数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model has a coefficient of determination, R^2, of 0.923.\n"
     ]
    }
   ],
   "source": [
    "# 计算这个模型的预测结果的决定系数\n",
    "score = performance_metric([3, -0.5, 2, 7, 4.2], [2.5, 0.0, 2.1, 7.8, 5.3])\n",
    "print(\"Model has a coefficient of determination, R^2, of {:.3f}.\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3 - 回答:\n",
    "答复：比较成功，从 $R^2=0.923$的值可以看出误差较小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第四步. 分析模型的表现\n",
    "在项目的第四步，我们来看一下不同参数下，模型在训练集和验证集上的表现。这里，我们专注于一个特定的算法（带剪枝的决策树，但这并不是这个项目的重点），和这个算法的一个参数 `'max_depth'`。用全部训练集训练，选择不同`'max_depth'` 参数，观察这一参数的变化如何影响模型的表现。画出模型的表现来对于分析过程十分有益，这可以让我们看到一些单看结果看不到的行为。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习曲线\n",
    "下方区域内的代码会输出四幅图像，它们是一个决策树模型在不同最大深度下的表现。每一条曲线都直观得显示了随着训练数据量的增加，模型学习曲线的在训练集评分和验证集评分的变化，评分使用决定系数R<sup>2</sup>。曲线的阴影区域代表的是该曲线的不确定性（用标准差衡量）。\n",
    "\n",
    "运行下方区域中的代码，并利用输出的图形回答下面的问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7zrrkPfusTdRz0UU2niket5nyJk601qmJE+18Y/D7rThzY5tycqzr3rffwpdf\nwqef2hTkX3xRk4K8qqrFT4OiKJ2IOXO6c/XVA9i8OYQxsHlziKuvHsCcOS1iofGyfPny8KBBg/Y/\n5ZRT9h0yZMj+69evD/7gBz8YMGrUqBGDBw/ef8aMGflu2TFjxgx7//3306PRKNnZ2QddccUVfYcN\nGzbyoIMOGr5x48YAwFVXXdVn1qxZeW75K664ou8BBxwwYuDAgaP+/e9/ZwKUlJT4jj/++EGDBg3a\nf/LkyfuNGjVqxPvvv5/ubVdhYaHfGENeXl4MID093bgCaf369YFJkyYNGjp06Mhhw4aNXLhwYSbA\njTfe2GvIkCH7DxkyZP9bb701r77je+qpp7ocdNBBw0eOHDnipJNO2q+kpKTFNU3nCtxx+0265x54\n7z3r996jR1u3SlEURWkMWVlWKG3dCt98Y8VRVlbtMiNGwG232ax4Tz9thdMVV1hhU1pa0xntpk02\nqQ/AlClNb0soVDtWyRhrbdqwoeYDXDBYE9+Unm6tTe3g662iKHuAiy/ux/Ll9bssLV2aSVVVbfNz\nJOLjZz8byGOP9Uy5zahR5Tz22DfNac6aNWvS/vznP6856qijygH+8Ic/bOjVq1c8Go1y+OGHD1uy\nZEnRmDFjIt5tysrK/Mccc0zpAw88sHHatGn73H///bm33XbbluS6jTEsW7bs83nz5nWdNWtWn+OO\nO+7L2bNn5+Xl5UVfffXVrz/44IP0CRMmjEzerm/fvrGjjjqqpF+/fgceeeSRJSeffHLxtGnTCv1+\nP5dccsmASZMmlVx//fXbotEopaWlvoULF2YuWLCgxyeffLIiGo3KmDFjRhx33HGlGRkZCe/xbdy4\nMXDHHXfkv/POO6uys7MT1157be/bbrstb/bs2XXavjt0LksS2D/OnBzrt75mjXY4qCiK0pHw+SA/\nHw480MaZbt+e+jnevTtceqm1It1zD+zcWSOQXCIRuPPOlmmXiBVBXbtai1O3bjauyu3wdvly6wq4\nbJkVeDt22P2rR4Oi7J0kC6RdLd9N+vXrV+kKJIDHHnus+8iRI0fsv//+I1evXp326aefpidvk5aW\nljjrrLNKAMaMGVO+du3alFlspk6dugPgyCOPLN+wYUMI4IMPPsg699xzCwGOOOKIikGDBlWk2vaZ\nZ55Z+9JLL60aM2ZM+d133937nHPOGQCwaNGi7J///OffAgSDQbp375546623sqZMmVKUlZVlcnJy\nEieccMKOhQsXZiUf38KFC7O++uqrtMMOO2z48OHDRz799NM91q1b12CcVXPoXJYksP0mXXihTQe+\nZIlNJztgQFu3SlEURWkKaWmjYktqAAAgAElEQVQ2sUNhof3gVV5urTbJcUGuK9706anr2bIFjj3W\nuuB5h759dz/GKBCwQ2ZmzbKqKttvk5tAyOer6bvJzaQXDKrFSVE6Oruy+PTpcwCbN9cVHfn5VXz0\n0cqWbk56enp1/wbLli0LP/TQQ70WL178eW5ubvzUU0/dt6Kios4DLxAIVH/F8fv9Jh6Pp3wopqWl\nJXZVpiHGjRtXMW7cuIqLL754+6hRo0YB66A6s1yj8B6fMYajjz665LnnnlvT1LY0hc5nSfL5bAKH\nHj1sbNLGjfYLo6IoitKxELHP8tGjITfXCqbKeuJ98/NTL8/Otn0zffkl3HcfXHklTJoEhx1mO639\n9a9hwQIbdxSJpK6jKYRCdp9ubFN2tm3zunU2pmnpUmtxWrIEPv/cxs8WFEBxsRWC0ahanxSlMzBz\n5kYccVFNWlqCmTM3tvaud+zY4c/MzIzn5OTE161bF3z77be77HqrpnH44YeXzZ8/Pwfgo48+Sl+9\nenUdS1VhYaHv5ZdfrvaZ/uijjzL69OlT5Wxfcscdd/QEiMViFBYW+o455pjSF198MaesrEyKi4t9\nr7zySrfvfve7Zcn1Tpw4sWzRokVZK1asCIGNj1q2bJlakhpFv342mPeuu6zrQ9eu9k9SMxMpiqJ0\nPIJB2wFtbi6sXm1d2bp0sR/FXK65xsYgeYVOWhrcdFNNTNLOnVYsffFFzfDss1acgK1v331rrE3D\nhtlxXl7z/z98vprseF7iceseWFhoRVIyaWnW8pSRYcfBYM2gVihFaf+4WexaKbtdQ4wfP758yJAh\nkUGDBo3q06dP5ZgxY+oIjd3luuuuK5g6deq+gwYN2n/IkCEV++23X0X37t1r9Z9gjJHbb789//LL\nLw+npaUlMjMz44888shagIcffnj9RRddNHDu3Lk9/X4/DzzwwNqJEyeWn3HGGdsPPvjgkQAXX3zx\ntrFjx1YsX768lgDq169f7IEHHlh31llnDYpGowJwyy23bDzggAOanTUvFZ0rBbiXL7+ECROsS8X9\n98OQIfYPVlEUpQ3QFOAtRDxek9ghFKrt6vbCC3D33TYDXX6+FU67StqQSNi6vMJp5UrrheCSk1PX\nXW+//VqvE1pjrIDyDiI1FqZAoKbT3MzMGhc+d9APgopSL3s0BXgnJhqNEo1GJSMjwyxbtiw8efLk\noWvXrl0WDAbbumlNpr4U4J3TkgQ11qTf/c66UQSD1qLUAS+eoiiK4uD329jTnBzrqrZ9u322BwJW\nEDU1k53PZ+NWBwywndW6lJRYsfTFF9Yt7osvbCY9N/V3MAiDBtUWTsOG2YQSyTRVvInUCJ5UuFao\noiLbAa4rnlxxFA5bC5RaoRRFaSWKi4v9Rx999NBYLCbGGO699951HVEgNUTnFUlpaXDBBfDkk9YP\n/dFHbSCtJnFQFEXp+KSnW2GyfbtN7CBiXfBaii5dbNzSYYfVLIvFrDDzWp3ee8/23eSSl2fTlLvC\nadMm28m56wa4u6nJwQodv9+KoWRcK1R5uRV6rhXKu61rhcrIsHW44skdfJ0vXFlRlJYlNzc3/tln\nn33e1u1oTTqvSAIriH74Q7j1Vhske/DB1uXO656hKIqidExE7DO9SxfrMldQYBMltJYbXCAAgwfb\n4eSTa5Zv315bOLniKTkluUskYvt6GjIEeve2lrCWcpFrjBUqHq9thfLu2xgrkrwufG6fUaFQbTHl\n99tz4vOpi5+iKJ2Ozi2SsrLgzDOtNemPf7SuEmvWwP776wNdURSlsxAKWde3nj1tn0U7d9qPYXsq\nPqdHDxg/3g4uVVW2LaedlnqbwkI49VQ7nZ5uxVJ+fu2xO52fX7dT3ebiipuGhKQxVkglEtYiVVpa\nMw91z6kxNaLKOw6FrIhKFlZqrVIUpQPQuUUSwMCB1pp0883w7rswdqz96qdJHBRFUToXXbrYTmi3\nbrWWkpKSmhd7sC/nrnUk0Mp/f6GQdbvr06emzyQvubnW7W7rVhurtHmz7dPp3Xdrxxm5ZGXVFlG9\netUIKHdZep0MvA1TX6yUSNPPj2uhikSsSE0k7LzXUuU9Jtda5RVV//oX3H67TZqxzz5wyy3wgx/Y\nsq6w0g+ciqLsITq/SOrSBU48ER5/3PqFP/ustSZpEgdFUZTOh5vYoU8fOx+NWqtOVRVUVNgXeNc6\nAjUv3YFAjYBqSStHfanJr7sOTjgh9TbRqHUddIWTO7hiasUK+7EvmW7d6ooorzWqd+8aC9ILL9Ru\n1+7GSrkWosbizeBXWQkvvgi/+U1Ne775Bq64wo4nT67ZxhW6rjUsWfi67n9eYaUiS1GUZtD5RZLP\nB/37w8UX2z+A116Do46CDRtsfxiKoihK58V9gc7MtBnxXBKJGvFUVWXF086dUFZWY31y43O82eGa\niis4mpLdLhi03Vf07Vt/mcrKGiuUV0S5408+sf1JJdOjhxVLX39dt/PcSARmzbLbG2PPQyJhp3c1\n39hy9W33xhup23PnndYi1aVLzZCdbS1rWVn2+rj1uXWlcgd0U6h7RZZXWCW7BtYnstz6XKuYO51q\nfldl6lvX2HP3z3/a8+PeVzNmwCmn1LQZaoShSP3T7vlq6enkQdltxo0bN/SXv/zlljPOOKPEXTZr\n1qy8lStXps2bN299fdtlZGQcXF5e/snatWuDl112Wb9XXnlldXKZsWPHDrvzzju/Oeqoo8rrq2fW\nrFl5V1999bfZ2dkJgKOPPnrwM888syY3Nzde3zaNYenSpeFLLrlkYElJib+qqkrGjRtXNn/+/HW7\nU2dL0PlFEtg/hUmT4IknbKa7yZPtn0vPni3n560oiqJ0HJI7efW6YMdida1ProDy4rVgNGRFaU5q\n8l0RDtsPgP3711+mvNyKpmSXvs2b4bPPUm9TUmI7Yk9Fqhft+uZTjRvaNlkguZSWWuFWH5mZ1jOk\na1croBqadkVWVpbdrrLSXt/6RJY7/fLLMGeOPY+9esFll9n3iOSEF82dT3atTCU2kpe/9FJty9um\nTXD99fZ4Tjihdp0NibNkXDGZfA4aOw3wyivw4IO1z9eJJ9rrHQjUuHN6xad33itU67uXGhJ9qZg3\nD264Adav50A4IHWhlmfOx3O6z3p7Vt8tZVtCvbN6V808aubGyw5rfmeyU6dOLZw/f353r0h65pln\nus+ePXtDY7YfOHBgNJVAaiwPPfRQr0suuaTQFUlvvfXWV82ty8uVV17Z/6qrrtp63nnn7QD46KOP\nmug7XJdYLEZgN92q9w6RFAjYL3LTpsEvfmHdDE44waZy1SQOiqIoihfXupCRYV3YXIypa30qL7dj\nb6pt9yWwrTt3zciwnd7ut1/ddRMnpo6Vys+3L7lNeQFtCRpqzzPPWKtYSYkd3OniYjt4p1evrpl2\n+7RKhZsy3h26das73bUrrFoFf/tbTV1btti4qZIS22G9m9AieRyLNX558nRjtnnmmdSWt9tus1bC\nZOHRkBBpyfk337SJsiora5+vykr47ndrW8WSrWNQe5zc/xekFnZewee1/IE95qQ2BaGV0l/WZs7H\nc7pf/drVAyKxiA9gc9nm0NWvXT0AoLlC6fzzzy+67bbb+kYiEUlLSzMrV64MFRQUBI8//viy4uJi\n3+TJkwcXFxf7Y7GYzJw5c5MrOlxWrlwZOvnkk4d8+eWXn5WVlcnZZ5+974oVK9IHDRoUiUQi1Sf6\n3HPP7b906dLMSCTimzJlStHvf//7Tb/5zW/yCgoKgkcfffTQnJyc2KJFi1b17dv3gMWLF3+en58f\nu/nmm3vNmzcv12nntpkzZxasXLkydMIJJwwZO3Zs2eLFi7N69epV9eqrr36VlZVV60IWFBQEBwwY\nUP2DHTt2bAVYoXPFFVfs88Ybb3QVEXPhhRd+e8MNNxT885//zL7uuuv6xeNxRo8eXf7444+vS09P\nN3379j3glFNOKXzrrbe6TJ8+fcv48ePLL7vssv6FhYWBtLS0xCOPPLLu4IMPrueLTF32DpEE1mo0\nYYIVRfffb7/qlZbaANm8vLZunaIoitLeEbEWHLd/oh49ata51qdo1L6slpVZ8VRcbNd7X+JSZXvb\n09QXK/Xzn9dY1/YUsRhcdZVNsJTcniuvtGIvO7vm3DVWrEUitYXUroTWxo010/EGvIcqK6375N13\n79ZhNxqv0HHHO3emLrtzJ8yfX1tgtTWVlVYo3X57W7ekRbn4nxf3W16wPKO+9Uu3LM2sSlTVulkj\nsYjvZ6/8bOBj/3usZ6ptRuWNKn/s1Me+qa/OXr16xUePHr3z6aef7nreeeft+Mtf/tJ9ypQpRT6f\nj4yMjMSLL774Vffu3RObN28OjBs3bvg555yzw1dPjOWdd96Zl56enli9evVnixYtSh8/fvxId93d\nd9+9sVevXvFYLMaRRx45bNGiRek33nhjwYMPPtjrrbfeWpWfn1/rxnrnnXcy/vrXv/ZYsmTJ58YY\nxowZM2LSpEmlubm58fXr16c9+eSTq4888sh1J5544n6PP/54zhVXXFFLJF555ZVbTzzxxKEHH3zw\nzkmTJhVfeeWV23Nzc+N33XVXz/Xr14dWrFjxWTAYZOvWrf7y8nL58Y9/vO9rr7228sADD6w8/fTT\nB95xxx09Z86cWQDQo0eP2IoVKz4HOOKII4Y+/PDD6w444IDKhQsXZl5++eX9P/zww1X1nd9k9h6R\nlJZm/9AuvRR+9jObwOGMM2DdOvvVqLX61VAURVE6P671CawFolcvO21M7eQRroiqqrIvj+50qjia\nZAuAO24JmhMr1RS8FpBYLHUKcddNKxSy+w0GbXs2bbLeH9deCyedZLePRu3gZs5L5brmxpB5rQk5\nOdaVsilJG4ypEbiTJqW2XoD94FqfpaY+i0tDy5MtPQ2Jwvosb3362Piu5GuRyiq1KytWc+avvrr+\n8+qKpFTH0xQrZX1l66t3xozG192CJAukXS1vLGeddVbh3//+95zzzjtvx7PPPtv9T3/601qARCIh\n06dP3+fDDz/M8vl8FBQUhDZs2BDo379/SqX87rvvZl111VUFAOPGjasYOnRodSzSX/7yl+5z587N\njcVism3btuDSpUvTxo0bV1Ffm958882sE088cUeXLl0SACeddFLRG2+8kT116tQdffv2rTzyyCMr\nAA4++ODytWvX1ukF+2c/+9n2U089teS5557r8sILL3SbO3duzxUrVqxYuHBhl8suu2xb0IkH7dWr\nV/yDDz5I32effSoPPPDASoCLLrpo+/33358HFABccMEFRQDFxcW+Tz75JGvq1KmD3P1UVTXt3Le6\nSBKRycA9gB94xBgzO2l9f+AvQDenzHXGmJdapTH5+bb39IMOsv6yp59uf0AbN2oSB0VROi3t6jm8\nt+GKgMb0S+SKCXdcWWkHV1B53fq8wgBSd/K6qxfPpsZKue102+i+dCeLHmNqkiGkpVnLWyhkx27b\nvEkS3O1HjrRCrSntSH5Rd8VUVVWNdS8SqWlvfTE1ySImFKrJDFifGDn22Mafu5amPktgqvPnTeQQ\nrvN+2rLccUf95+v736+73Otyl+yC546h7nxTBG/v3tbtr4VpyOID0OeuPgdsLttc54efn5Vf9dEl\nH61s7n7POeecHTfccEO/d999NyMSifi+853vlAM89NBD3bdv3x5YtmzZ5+Fw2PTt2/eAioqKJn9V\n+eKLL0L33XdfryVLlnzes2fP+BlnnDEwEok0++tMKBSq/srg9/tNfW0aOHBgdPr06dunT5++fciQ\nIfsvXry4WXFJbrxUPB4nOzs79sUXX6xoXstbWSSJiB+4HzgO2AB8LCLPG2O8Db4ReMoY86CIjARe\nAga2SoOysqzJ/oorrEXpqafg3HPtj0eTOCiK0glpd89hpS5uDFNjgoxdMZAsqLzWqaoqGytV3wtl\nsqASSS063G1dQeEKvnDYusClpdX0c+SKHnfcmp3FNuV8eWnIAuJa+bwiq6rKvivMnl1XjFx6ae3s\nganiZ9zljV3WlO0nTLBp5JMTSnznO7ZdqbZLRWOTMTR2mx//2FqM6jtfyeJepK7lzHsP1RdH1dik\nDj6fzQB46aX2N7EHmXnUzI3emCSAtEBaYuZRMzfuTr1du3ZNHHHEEaXTpk0bePrpp1e7rRUXF/tz\nc3Oj4XDYvPDCC9mbNm1q0EVqwoQJZfPmzet+yimnlH788cdpq1atygAoKiryp6enJ7p37x7/5ptv\nAm+++WbXo48+uhQgMzMzXlxc7MvPz69V18SJE8suvvjigb/+9a+3GGN46aWXcubOndvoBBFPP/10\nlylTppSGw2Gzfv36wI4dO/wDBgyomjRpUslDDz2Ue/LJJ5e47najR4+ObNy4MbR8+fLwqFGjKh9/\n/PEe3/nOd0qT6+zevXtin332qXrsscdyLr744qJEIsGiRYvSjzjiiHotYsm0tiVpLPCVMWY1gIj8\nDTgV8P45G6CLM90VSPEJogXZZx844ABrUZozB8480z7sV6+GUaO0F3BFUTob7e85rDQf9wWxMenI\nva5u7tgVAJGIna6stOWCQftf6Fp/3D6IksVPR6Yp587l4INt4osbb7R9Nnk7uU1FU1zJGlu2vu0P\nOwz+7/9q5pPdAlNluGtouhHlEiZBPB6zYxMnkYjXmo5eeg7B7ul0mf17Apu2EOvTm6LrplMx9TQQ\nwSd+xO9DxIfPH8Dns/eUT3yICIJUj+2hN3XaOFNxW1ciAWefhc8k8N1gr2HUmAYyerQcbnKGlsxu\n53L22WcXXnDBBYPmz59fLUSmTZtWeMIJJwweOnToyAMPPLB83333bTBBwYwZMwrOPvvsfffbb7/9\nBw8eHBk5cuROgCOOOKJi1KhR5YMGDRqVn59fNWbMmOq0nhdeeOG3kydPHtqrV6+qRYsWVcf2TJgw\nofycc87Zfsghh4wAm7hh/PjxFStXrmxULMsrr7zSZcaMGf3D4XAC4JZbbtnQv3//2NVXX71t1apV\n4eHDh+8fCATMhRdeuO3666/fNmfOnLVTp04d5CZumDFjxrZU9c6fP3/1JZdcMuC3v/1tfiwWk9NP\nP72wKSJJTH2+ti2AiJwJTDbGTHPmzwfGGWN+4imTD7wG5ACZwLHGmCUp6roUuBSgf//+Y9ata2b6\n9EQC/vc/WLYMLroIfvUrOy4stA9CTeKgKEorICJLjDGHtsF+W+w57JRtmWexoih7nIRJWFHjFTee\nZbFEjGgiSjQerZ6OJWLV8y5SbT1yRmLAWLHj9/mrxQ6AMQZDzbum+95pMHWmDcYKHjdRpLHTxtRe\njqHO8sZMC8LYfmM/MQlzSHPO39KlS9eOHj362+Zsq7Rfli5dmjt69OiBycvbQ+KGHwBzjTF3icgR\nwBMiMsoYk/AWMsY8DDwMcOihhzZf2fl8NiA0GoUjj4SHH4azzrLpPteu1SQOiqLsjTTqOQwt+CxW\nFGW38IoXV+x4xU00HiWeiFOVqCKeiBNNROuIjep5IEECv/jxia968Pv8+MVPMBjE7+vglkSgqKKo\nrZugdCBaWyRtBPp55vdxlnn5ETAZwBjzgYikAbk4WSpahR49bFa7n/7UmsyffNL6q/p8sGFD6j4l\nFEVROibt8zmsKEqDuCLIFTyRWITyaDkVsQoqY5XWCuOxrBhMtbgRkWrB4xc/wUCQDMmosQApirJL\nWlskfQwMEZF9sX/KZwPnJJVZD0wC5orICCANSOlb2GIEAjZjjc8HRx8Njz4K55xjkzoUFNgkDtnZ\nrdoERVGUPUT7fA4rbU48EacyXkk8Ecfvq3mhdqeV1sUrgmKJGBXRCsqj5URiESKxSB03tYAvQMAX\nwO/z0yXcRQWPorQyrSqSjDExEfkJ8Co2rexjxpjPRGQWsNgY8zzwc+BPInI11sv0ItOagVIuPXva\nNJU//alN3vCXv9R0WrdmjSZxUBSlU9Cun8PKHsEYQ1W8isp4JRXRCkqrStlZtZNILFIrbgRsrIkx\n1iIR8AUI+oKEAiECEiDoDxLyhwj6g7UEVbXFQsVVLWKJWC0hVBGtoCJWQSQaoSJWQcLxZnXPuSuA\nAr6AiqD2SyKRSIjP59PnYychkUgIUMe1HPZATJLT18ZLSctmeqZXAONbux11SEuD7t1tlptjj4U/\n/xnOO892AlhUBN9+q0kcFEXpFLTb57DS4kTjUSrjlVTFqyitLKWsqoyd0Z21RFDIHyLoC5KTnlNv\nPcaY6sD+SCxCPBHHYKoD/t26vAH0YAP3Q/4QfvETCtj9uPsL+AO1LFVekdURBUGyCIpEI5THymuJ\nIG/yABVBbccLK1/g7g/vZnPpZshj/92oavm2bdtG9uzZs1iFUscnkUjItm3bugLLU61vD4kb2o78\nfNi+3VqTXn/dCqXp062rnSZxUBRFUdop8US82jpUXlVOaZUVRLFErNoy4Vp+uoa7NvmF3I1p8dO0\nYH1jTHUSgUgsws7EzmqxZYypFlYGz7QYfPiqrVRBXzClRcqb8nlXbRd2fbzeDGz11ePdr8FQGaus\ntggliyBXAO3tIsgrSPKz87nm8GuYMqwJnRa3QntufONGIjEnI7aPZr/YxWKxaVu2bHlky5YtowA1\nm3Z8EsDyWCw2LdXKvVskZWXZITMTJk+2LncXXGAtTH6/7RNh0KC2bqWiKIqyl+J1lauMVVJSWUJZ\ntIxItKYLFL/PT8gfIiOY0eYZyESEgDT91cIrrsqj5bVicbxlGqwjxTa7Q6r9uUIoO5ytroUpSBYk\nm0o3ceMbNwK0iFAyxlQnsqiKVxFNOGPHmudOe9fd+s6tNQJpNxkzZkwBcEqLVKa0e/ZukQS2Y7iV\nK6016dVXbRKHX/zCiqeCAutyp0kcFEVRlFYmlohRGaukMl5JWWUZZdEyyqrKarm0hfwhQv5Qg65y\nHZFa4qrjZ5reKzDGEIlFKKksobiymJLKEm5797Y6giQSi3DTmzexdOvSOsJmV/Op1inKnkJFUpcu\nNi5pwAA4+WSYNw9++EPIzbUWptWr4YADNImDoiiK0iIkTILKmI0bKo/WuMpF484LoFAdy9McVzll\n76Cl3NoisQjFkeJqoVNcWUxJpEb4JC+rLhcpbrRo2RndyT9X/rMmRs1fE6vmTqcF0ugS7lJrXXVZ\nX6hW4hB3vqF1bh2Xv3g528o1WafSdFQkuZ3Lrl0LP/kJvPSS7WD2+uttcofCQti2DXr1auuWKoqi\nKB0MY4yNG4qWUxwpprSqlIpoRXUsi2ATKaQH0skKZbV1c5UOQiq3thsW3sDmss0ckn9I/eImhfip\nilc1uK/sUDZd07rSNdyVLuEu5GXm2em0LtXLuoTt9C9f/yXfln9bp44+2X1448I3WuVc7Iprx19b\nOyZJURqJiiSo6Vy2f3849VSYPx9+9CMrjLp2teu6dYNwuK1bqiiKorRjjDFUxCoorypnR+UOdkR2\nEE/EAaq/bnc2VzmldTDGsCOyg4KdBTVDuR0/+/mzdV76K+OV3PXBXSnryg5lWyGTZkXN4MzB9Qod\nb7nsUHaT4tyuG39dHUGSFkjjmsOvad5JaAFc65prdTMJ07AqVBQHFUlQ07nsli1wxRXw/PMwZw7c\ndJNN4ODz2SQOgwe3dUsVRVGUdkTCJKo7AS2KFFEcKa5OkR0OhMkKZbXbAP/2mIWsPbWntTDGsDO6\nk607t9YWQCmGVFaebmndGrSKPHbKY7sldHaHZEHSXq7jlGFTmDJsCkUVRRx+y+GftWljlA6DiiQX\nt3PZffaBM86ABQtg2jTriteli3W5y8uz04qiKMpeSTwRpyJWwc6qnRRWFFJaVWpTWyOEA+EOk/Ws\nNbKQGWOqM8y5meEMpta0t6x32YurXmTW27NaLSva7tAU8VYRrWBb+Ta2ljUggMoLKI+W19k2K5RF\nXmYeeZl5HJJ/SPV0rSEjj3AgzMS/TGRT6aY6dfTJ7sP4/m3b5ZkrSBSloyMdsVP1Qw891CxevLjl\nK/7ySygttcNxx8Hpp8Ovf23XVVZCIqFJHBRFaRYissQYc2hbt6MlabVncTsilohRHi2nrLKMokiR\nzTaHzcaWFkgj7A93mMQKZVVlfLn9S1ZuX8lv3/ttyhd1sEkjvKQSOl5B1NpkhbJSBvw3Zr4523iD\n/99d/y5/XPRHKuOVtc7PSUNOIi8zr5YLXMHOAkoqS+q0Py2QVkfo5GXm0SurV/Wynhk9yQxlNvqc\n1On7x9nPbyb+RgVKAxRVFHF4/8M/MQlzSFu3RWn/qCXJi9u5bH4+nH22jU269FLo18/GIxUVaRIH\nRVGUTkxVvIqKaAWlVaUUVhRSEa0AwCc+0oPpHSKeKJ6Is654Hau2r2Ll9pWs/HYlq7av4puSbxq1\n/UUHXVSn41Y30UT1NLU7bU3V0at32+SOWZM7cq0vlgbgjBFn7DJV9M6qndXzqcrsKjlBU4gmojy3\n8jmCvmC1yBmUM4gj9jkipfUnO5Td4kK6vbq1KUpnQkWSF7dz2cpKK46eegruvx9mz7bru3TRJA6K\noiidiMpYJRWxCoojxRRVFFEZr0QQ/D4/4UC43YuiworCaiG0crsVQ18VflVtYfCJj4HdBjIqbxRn\njDyDoT2GMqzHMM5/9nw2laV215px5Iw9fRjMXz6/Xvex679z/W7Xb4whlog1qY+eqngVV71yVcr6\nBOHTyz9tU9dKdWtrPPGE7ajYjRdUlMagIikZt3PZvDw491yYO9cKpv32s0kc/H5N4qAoitIBcdNx\nV0QrKKooYkflDmLxGAZD0B8k7A+TEcpo62ampCpexdeFX9eyDK3cvrJW/y890nswLHcYPxj1g2ox\nNKj7INICaXXqu+aIa9pVFrJrDm/d9ohIdb86GcHGX+M+2X1Sirf87PwOEXvWmXBFTsIkaomeuInb\nuEDXWmesW6iIVLuJhvwh/D4/PdJ7sMf8RJUOj4qkZNzOZaNRuOQS+NvfrDXpLscVIDtbkzgoiqJ0\nAHaVjjstkEYg1L7+Bkr81KwAACAASURBVI0xbC7bXMsytHL7StYUrSFubNtD/hBDug9hQv8JDOsx\njGG5wxjaYyi5GbmN3k97c9dqb+1xaW3xtrdhjCFu4rUEj1f0SI1PZ3VCFMTqmoAECPgDBH1BwsEw\nAV+gOpYs4A/gFz8+8eH3OWPPfHIz9vRxKx0TTdyQiq1bbeeyOTlw9922c9nnn4ehQ+16TeKgKEoT\n0cQNrU8sESMSi1hLUaSIHZEd1V+Sw4EwYX94j6VC9lJfdrSyqjJWbV9VJ3aotKq0etu+2X2tVSh3\nmBVEPYYxoNsAAr72Je46M3tLavKm4AobgyGeiFdbc2pZdQzVcWyudccnPitqfAGC/mCN0PEHCfqC\nKQWOO91ScV2d8VmstA4qklIRi8F//2stRSUlMGkSjB8Pf/xjTZmiItv5bH5+67VDUZROQ2f8Y25L\nkZQwCSKxCJFYhJLKEoojxdVf+9103OFAuM1dolJlIfOJj67hrhRFiqqXZQYza4SQYxka2n0o2eHs\ntmi2speQ7LbmtewgIEaqXddcoYNQy6rjZgV0BU/AF6hj0XGn2/r3CJ3zWay0DvopKhXezmW7dYOL\nLoL77oPPP4cRI2yZLl1g/Xro3l2TOCiKorQixhiq4lVEYhFKq0opjhRTFi1DsC9wQZ+NJ2pvSRai\n8Si3vnNrnY4/EyZBRayC6YdPr7YO9cnu02FSiSvtC2NMrdicZLHjdWFLjtdxLTohX4h0f3otwRPw\nBWpZclrDqqMo7RkVSfXhdi5rDFx4ITz+ONxzD8yZY9f7/VZMrVtX44anKIqi7DbReJRILEJ5tJzi\nymKKI8XVWakCvgDhQJhu4W7t9kVt7Y61PL3iaf7xxT9qWYu8VMYqufzQy/dwy5SOgOu+5o3VcQVQ\nfbixOUFfkAx/Ri3LTiqLjoodRdk1KpLqIy3NWolKS63V6OKL4Q9/gE8/hQMPtGWys22/SiUlmsRB\nURSlGcQT8eo4opLKEkoqS6o77hQRwv4w2eHsduGm0xCRWIRXv36Vpz97mo82fYRf/Bwz8Bg+2fIJ\nhRWFdcrnZ+99rtrJL/9uPEvCJGrHsDhawE1C5n2Rdzu2dV23fOJDkJppp+8m73xbkix0aiUpSD5m\nJ1mBK3jSAmlW+DiWHjc5gWvR8Y4VRWl5VCQ1hNu5LMD559t04PfcA48+WlMmKwtWr7ZJHPz6oFIU\nRakPNwV3JBahtLKUHZEdlEfLq93mQv4Q4UD7TcOdis+3fc6CFQt4fuXzlFaV0r9rf35+xM85bfhp\n5GXmpYxJ6gzZ0VJZO9yhoZf/gC9AyBciGLRuXW7QfqqXfrc+150sORta3MSJJWLVQzwRJ0GCWCJG\nVbyKWCJWbYGsdjlz8AowY33QqkVVQ8IrOX7HPfaUOLE7rshxLTz1HbNadxSlfaEiqSGysiAz02az\ny8qyKcHvuAOWLIExY2yZcBh27ICCAk3ioCiK4sGNI9pZtZPiSDElVSXVLkOu21x7iyNqDKWVpfzr\ny3+x4LMFfLbtM0L+EN8b9D2mjpzK2L5ja1m92mtqay8NuXftSvCE/WECwUC7ffl3BZahrtBKFl+u\n2PJOxxOOEDN2ud/nr1fweON23LEKHkXpuGh2u12xY4ftXDYnByoq4NhjYdAgG6PkEo9bl7vRo62b\nnqIoShKdMaOS91nsus25cUQllSXEEjHAk23OH+6wL43GGJZsXsLTK57m5a9eJhKLMLTHUM4aeRZT\nhk2hW1q3tm7iLkmYBNF4tNrKAtaSUm3dcdIwB/3Bdil4FKUl6IzPYqV16LCWpOJIMV3CXVr/Qe3t\nXDY9HS69FG67DT78EA4/3JZxkzisX69JHBRF2WswxrBuxzqKK4upiFYAVhCFAiHSA+mdIlaisKKQ\n5754jgUrFrC6aDUZwQxOHXYqZ448kwPyDmi3YsEVQ1XxKsBeFxEhK5RFTnoOmcHMNu07SlEUpb3T\nYUXSqu2r6JHRg4HdBrZuQK/PB3371nQue/bZNibpj3+EcePA/YN0kzgUFtpy7fSPU1EUpaVImAQb\nSjbQNa1rh3Sbq494Is7737zPghULWLhmIdFElIN7H8xt372NyYMnkxnKbOsmVpMwiWoxFE/Eq5en\nBdPoGu5KdjibtEAa4UCYoC/YbkWdoihKe6PDiqSESbBt5zZiiRiDcga17pewHj1squ9EwsYgXXYZ\n3HILvPceTJhQUy47G1atslal3Fy7XWamFVqKoiidkIDfxqN0BjaVbuLZz5/lmc+fYVPpJrqldeO8\nA8/jzJFnMrj74DZtmzGGaMJah6LxKGBd5fziJyuURY/0HmSGMgn7w9VpnxVFUZTm02FFEkBOeg7F\nkWJWbV/F4O6DCfqDrbMjb+eyXbvCmWfCI4/YTHfjx9dYjUIhmzY8HrdWpa1brUDyCibNgKcoitJu\nqIpX8caaN1iwYgHvrn8Xg2F8v/H84shfcOx+x7aJAIwn4tXWoYRJ2Ox/YsgIZJCTlkN2KJu0YBph\nf7j1/vcURVH2clpdJInIZOAewA88YoyZnaLMWcDN2Nw5S40x5zS2/q5pXSmtLOXzbz9neO7w1vtD\n83YuGwrB5ZfDjTfCm2/CxIm1y/r91qoE1vpUVGSz34lYsZSba7PlqWBSFGUP0NrP4Y7I6qLVPL3i\naZ774jm2V2ynV2YvLj/scr4/4vv069Jvj7Qh2TrkZpIL+ANkhbLIy8wjI5hBOGCtQ+29ryhFUZTO\nRKuKJBHxA/cDxwEbgI9F5HljzApPmSHAr4DxxpgiEclr6n6yw9mUVZXxWcFnjOg5grRAK2SY83Yu\nm5UFp50GDz9sY5OOOab+GCSfz5YHK5iKi+Hbb+28VzAFOrRRT1GUdsqeeg53BCqiFbz69assWLGA\nxZsWE/AFmDhwIlNHTmVC/wmt7qJWEa2wHeW6KbWBjEAGPdJ7kB3OJuwPEw6ECfj0/0BRFKWtae0n\n8VjgK2PMagAR+RtwKrDCU+YS4H5jTBGAMaagOTvKCmVRHi1n+dbljOg5onUCa72dywaDcOWVcO21\n8O9/w/e+t+vtvYLJGCu4tm+3Aisnp0YwBdV9QlGUFmOPPYfbmhdWvpCyP6LPCj5jwYoFvLDqBcqq\nyhjYdSAzjpzBacNOo2dmz1ZvV0W0gvJoOTnpOfTO6k16ML06dkgTKSiKorRPGi2SRGQo8CDQyxgz\nSkQOBE4xxvymgc36At945jcA45LKDHXqfw/rCnKzMeaVxrbLS0Ywg0qp5LNtnzEidwTZ4ezmVFM/\n3s5lw2GYMgUeegjuvdf2n9SUBA0itq7M/8/evcfJVdaHH/985z57381u7gmBECAkCMGggLaKRcUr\nVsWqaK1Sqa1X8qv8qFjvtV5eUlt/tBbRVq2tiD9/FaqoVcFqAshVmCQEAgESkrCbZDfZ21zOnO/v\nj3Nm9uzuzO7s7szuzu73nde8Zubc5pmZzTPne57n+T6NXsA0OOh1y1OFtjYvYGpu9rr2GWOMbxp1\n8azWw3Pl1j238tHbP0raSQNeEoa/+sVfcd1d13Gw/yDxcJxLTr2Ey868jK0rt85KcJJxMgxkB2iN\nt7J56ebq/yYZY4ypmal0cP4aXneMHICqPgS8uQpliAAbgBcDbwG+JiLjZuUTkStF5F4Rubenp6fs\nweKROA3RBnZ276R3uLcKxRtjzRoYGvIeh8Pw/vd7Ge1uu236xxSBhgYvOGprg3QaHn8cHngAdu/2\nuudls9UpvzGm3tWiLq6oHobRdfGRQtfheeC6u64rBkgFOTdH92A3H3vRx/jNu37DF176Bc5bdV7N\nA6RcPsex4WO4uJzZdSYbu2pw0c4YY0xNTSVIalDV345Z5kyyzzNAcATsan9Z0AHgFlXNqeo+4FG8\nH+tRVPUGVd2qqlu7uibuHhELx2iON/PIkUfoGSwfUE1LcHJZgEsu8SaQ/cpXwJns46iAiDdpbVub\n1wUvlxsJmHbuhJ4eryXLGLNYTbUurlo9DKPr4s7OzikWvXYO9R8quTzv5rn8rMtpibfUvAx5N0/v\ncC9pJ82p7ady1tKzaE20Wpc6Y4ypQ1MJko6IyHq8IaeIyBuB0r9KI+4BNojIySISw7vaecuYbf4T\n7+olItKJ1+3jiSmUq6RoOEpboo3Hjj3GwRMHZ3q4EYXJZQcGRp6///2wb5+XDvyMM7xsd7feWp3X\nSyS8YKm93Uv88MQT8OCD8PDDXorxdHryYxhjFpKp1sVzVg/PphXNK6a0vJpcdekb7qM/28/a1rWc\ns/wcOhs7LRudMcbUsakkbngvcANwhog8A+wDLp9oB1V1ROR9wE/x+rl/Q1V3isingHtV9RZ/3ctE\nZBeQBz6sqken8V7GCYfCdCQ7eOr4U+Q1z+qW1dW5ohecXDYU8gIVEejr89YfPOilBwdv3FK1xOPe\nDbzud08+6T1OJGDZMm8Op2Syeq9njJmPplQXz3U9PFu2nb9t1JgkgEQkwbbzt9XsNVWVgewAjuuw\nsnkly5uW27xFxhizQIiqTr6RSAh4o6p+T0QagZCq9te8dGVs3bpVr//P62lPtle0vapybPgYy5qW\nsa5tXXWu7u3fPzK57EUXeYHRWMuXw69+NfPXmkw2642TUvWCqKVLve56yWT51OTGmFklIvep6tYZ\nHmNe1cVbzt2iX731q7QnKquLa+3G+2/kizu+CMDK5pXF7Ha1MJAdIOtk6WrsYlXLqtpMPWGMqbpq\n1MVmcaioJUlVXRG5Gvieqg7WuExVJyIsaVhCz2APjuuwvn39zOfDCE4ue6hMT5fDh+Gyy+CCC7zb\nli1eq0+1xWIjWfCyWXjmGS+Ii8VGtzBNJfueMWbeqfe6uNYK6bxvfcutnLbktJq8RtpJM5gdpCPZ\nwRmdZ9AQbajJ6xhjjJlbU+lu93MR+UvgJqD446yqx6peqhppT7bTl+7j0aOPsmHJhplN2BecXHbF\nitItSU1NXga8G2/0UoXHYl6gVAiaNm+u/iSywYAplxsJmMALlBobvXIlEl6rUyxmwZMx9aXu6+Ja\nSXWnSEaSnNJ+StWPnc1nGcgO0BRrsnTexhizCEzlDP2P/Pv3BpYpUP1foxpqS7RxIn2CXT27OKPz\nDGLhGcxDtGKFl5572zZvDFIwiUIiAZ/4hDcmaWAA7r0X7roL7rwTvvxl79bUBOed5wVM55/vZcmr\nZve4aNRrRSrI5UYmsC1Q9cra2OjNy5RIeIFTPG7BkzHz04Koi2sh1e1NJj6jC2BjOK5Df6afeDjO\naR2n0Z5st2x1xhizCFT8S6KqJ9eyILOpJdHCQHaAnd072di1cfp9yZuavNvLXuY9v+46r+vdihVe\n4FRI2tDUBC9+sXcDOHbMC5gKt9tv95YvWeIFS4Wgac2asa84M9God2sY0z0kl/MCud5eLxmFyOjg\nqanJa4UqtFKFZ9hV0RgzbQupLq4mx3XY3bObyzZdVpXj5d08J7IniEiEk9tPprPBstUZY8xiUnGQ\nJCJR4M+B3/cX3QH8s6rmalCummuKNTGUGyL1rHflsTHWOL0DrVkDe/Z4AVGlmew6OuCVr/Ru4HWJ\nK7Qy3XUX/OhH3vLVq0cCpvPPh1rNSVIInsZynJHgSdW7gdfKFAyeCt32LHgypuYWWl1cLU/0PsGw\nM8zmpZtndBxV5UTmBIqypnkNS5uWVrVlyhhjTH2YSs3/T0AU+Ef/+dv9ZX9a7ULNloZoA2lJs7Nn\nJxs7pzkjenBy2VKBRiVWrYI3vMG7qXqTxxaCpp/8BG6+2dvutNNGWpqe9zwvSKmlSKT0mCnH8bLp\n9fWNbnmKxbwyNTZ6rVWFlqdqj7syZnFbcHVxNaS6UwAzCpL6M/3k8jlWNK9gRfOKmXXHNsYYU9em\ncvZ6nqqeHXj+SxH5XbULNNsSkQQhCbGzeyend55ecVrxosLksk8+6U34OlMicOqp3u1tb4N8Hnbu\nHAmabroJvvUtr9XmrLNGWpnOPXdkDqVaKwRPY+dkKgRPx4975S68n2i0dMuTBU/GTMeCrItnKtWd\noiHawMltU++NOJQbYjg3TFdjF6tbVls6b2OMMVMKkvIisl5VHwcQkVPwJh2se7FwjOZ4M48ceYQN\nHRvobJxit7axk8tWUzgMz3mOd7vySshk4MEHR7rmfe1r8NWveoHHueeOZM4780y47bby46RqoVzw\nlM/D8DCcOOF9RuC1PEUiI139CvsGn4dC3vsPhUo/NmZxWrB18UykulNs6to0pXFDGSfDQHaAtkQb\nGzo2TL/btTHGmAVnKkHSh4HbReQJQICTgHfWpFRzIBqO0pZo49Fjj5Jzve4WFYtEvCCkMLlsLcXj\n8Pznezfwxgzdc89I0HTddSPb5XIjQcnBg14GPqhtoFRKOOwFTqWCJ9f1WqCyWe9x8AYj2f5URz+G\n0YFVJOK9TjDICocnDrIsQ5WpTwu6Lp6OXD7H7iO7ufysyyvaPpvPMpAZoCHWwKalm2iJt9S4hMYY\nY+rNVLLb/UJENgCn+4v2qGqmNsWaG+FQmI5kB/v69uG4DqtbVlee6jU4uexsnnw3NcFFF3k38NJ7\n3303XHut1+oUlE7DNdfAD37gTTK7bBksXz5yv3y512VwtspfCGKmKxhQpdPjg6yxgdXYICsUGh9k\nFdKfF7oDFpYX7o2ZY4uhLp6qvcf2ks1nJx2PVEjnHQvHOG2JpfM2xhhT3lSy270X+I6qPuQ/bxeR\nK1T1HyfZta6EJMSS5BKeOfEMOTfHurZ1lXXfCE4uW+uEChNZssTLmrdtW+n1hXFDd90F3d0jY4cK\notHRwVOpQKqzc36MJyq0Ck2X6khrVi7nBVr9/SPLgidPqt5rxWIjc0kV7gvBVDCgMqZGFktdPBWp\nnomTNrjqciJzAkFY17aOzoZOwiG76GGMMaa8qZzNvVtVry88UdVeEXk3IxmWFgwRoT3ZTs9gD47r\nsL59fWU/qKtWeenAe3tHTrDnKsPbihVey9ZYK1d6yR/ACwaOHIFnn/Vuhw97t8Lzhx+Gn/98fItU\nKOQFSsHgKRhQFZ6XSyRx662zO1aqHJGR76WSMql6QWYmA4ODXiAVTFBR2EZkpDUqHvduicRIABUM\nqOwqtpm6RVMXVyrVnaI51sza1rWjlqsq/Zl+8ppnVcsqljctt3TexhhjKjKVX4uwiIiq11dJRMLA\ngs2PWgiU+tJ9PHr0UTYs2TD5j2tDA2zZMtIqkU57md76+72xQ4UT6GjUO3GuZeC0bZs3BimdHlmW\nSIxuYQqHR4KbclS9VN/lAql9+7xWqf7+8fu2tY0PpA4dgh/+0BuDBF4gd+213ud08cUjr1m4L/U4\nWLZy2wbvJ1qvCr/6FVx//UgwWG78VuG7myzVe6GFynFGugE6zkiq9GBgVPhbKLRMxePesrEBlQVT\nZsSiqosrkepOsWnp6KQNA9kBsvksyxqXsbJ5JfHILGX/NMYYsyBM5Sz9J8BNIvLP/vM/85ctaG2J\nNk6kT7C7Zzend55e2bwZhRPp5mZvrBKMnDAXsrz1948OLAony9Oda2mswsn9TFtsRLxxSu3tcMYZ\n5bcbGPCCpu7u0YFU4f7hh+HYsdL7ZjLw6U97t/kinYarr4YbbvCCvcJnMNEtmfQ+r0ILVSQyeVr2\nfN67DQyMTp0OIwFVIRNgcPxU8HEhqAomp6hyJkBVJZvPkslnGM4N05/tZzA7SDQcpTnWTFOsiVg4\nRjwStyv1tbco6+Jysvkse47s4R3nvKO4rHe4l/ZkO2ta1pCMJifY2xhjjCltKmcz/xu4Em+md4D/\nBm6seonmoZZECwPZAXZ17+KMrjOmN4dGJOKNVWpqGh04ZTJe4NTf7wVPg4MjLR3R6Eh3vSlw1SWd\nz5B+2e9x/KItnMgNkHWz3lXWY6nidiFCgceBlgqRUYOZg+sECW5JKDAmSLoE6VoOm5b72waOLwLZ\nLKe84FXImAYhAAX0k5/0jiejy1K8Dz4O3ley3diWmOCyD35wfIHAawFau9brPvnYY9798eMjmffG\nisdHAqZKA6t4fCRhRCnBboDLl3tlfcUrvGBKdXSXv7GfCZTPBBi8j0ZHBVY5zZPBIePmGHCH6c8O\nMuQM+YdTRIRYOEY0FMVVl56hHg4OHERUQCAWitEUb6I51kwymiQejhMLx2yAfPUs2rq4lEePehlJ\nx45HOqn1JGs9MsYYM21TyW7nAl8FvioiHcBqVV00c3M0xZoYyg2xs3snG7s20hBtmPlBCyeujY3e\nGB/wTnjTaS94OnHCu/X2juwzJnBSVbJujnQ+Q39ugOO5AQbzQ4CgqkRDEeKhGIlIE8pIdBJ87B2H\nkuvGb+eOWpLPuyW3LXl8gdyyLmKHe8Z9FNllnTx48UYSoRgNkQaaIkmSkQSxUJRYKFrb1omVK8uP\n37r++tHL8nnvO+nr876Xcre+Pti1aySwKqehoXxQtX+/FyTlct62hw7Bxz/uBUavf/3UuuAVgqlC\nJsDBQfKuQyaXJutkGHSGGXDTDOSHcFyneOwoEWKhCK2ROOIHVs3//T90/uO/EDncg7N8KUeuupL+\n117iBVoiOO4gAyeOcyyfHQlGRWiKNnqtTvEm4uE48UiCcDgyPpgtF/CWW7fILPa6eKxUd+mkDdai\naYwxZiamkt3uDuC1/j73Ad0iskNVr6pR2eadhmgDaSdNqjvFxs6NNMebq/8i4bAXNDU2etnywDu5\nzWQgnSZ3/Bjp3iMMHTvG8fwQJ7L95CMhJBIlHI0TD8dojTSXvGo/ug1o3MpZcfQv3smyz/49ofRI\nMgg3EefYe99Fe6wFx80z6AzRlzuO67daoEokFKExkqQp0kBDJFkMnqKhKnRPrGT8VkE4PBLEnHxy\nZcd3HC9Qmiyo6u31JiXu7fW635WSycBHPgJ//dcjfydNTaPvg7emJrSxEScZJ9eYIJ2IMJgIMRAT\nhhMR3KYG3HiMUDxMLBSnMdRUOpujn1a9+bZfsuxz/4eQP34reuhZlv3156HvOP0X/573XeFVEsXL\nCCJ+MH+IbnU46Prn86okwnGaIg00h5MkwnHioRixUNT7c5wofXvg2Ih4AdpPfuIFtYcPe61uH/oQ\nvO51I10PgwkzCssK+070eKYTRH/nO3DttTwXnjuzAxXestXFQanuFG3xNlY3rwa8C0chCVn2OmOM\nMTMylUttrap6QkT+FPiWqn5cRB6qVcHmq0QkQUhC7OrZVZxno1bybp60k2Y4N8yJzAmOZ46TjWZh\nqSBdHcTzHTTlhdDQsHdSPTwMpEEyI+mq59mg//5XvASAzn/8VyLP9uAs6+LIX/xJcXkkFCYSCgOj\nuzTmNU8mn2UgN4RTvGiuhCVMYyRJY6SBpkjDqOCp4u5dr3mNFwT83d+NnGBfdRW8+tXVedORiJea\nfcmSyvfJZuE5zxkfFBS8+91e18zBQe+7HxyE/n700CF0cBAGB5DBIcR1ESDq3xqAjjGH0lAItyHp\n3RobvFvh8ZhlHd++uRggFYQyWbq++i2ck9Z4AVFIAIGQF8CoH8gkis9DXlAuIfJkydLHYc2DgIog\noRAN0SSN0UYaog3E/O56xVanQhBTGK8VCsGPfgR/+7cjgW6h1S2X87onFgKt4BxaBWMDsLGp32Ek\nyCp0SywEW8Hgq/C4kLUwFILvfx/e/37//2bVWF0ckOpOsXnp5uL/d8d1rJudMcaYGZtKkBQRkRXA\nm4Bra1SeuhALx5CY8MiRR9jQsYHOxs4ZH1NVRwbFZ/o5njnOUG5o1GvGI3EaY43jd27zAzXX9U6u\nMxlvLqSBAW+sU6lxKsEr8GNPPIOPaxBg9b/iJcWgqFJhCRMOh0mER5/8uOqScx2ODB/lkHsYyfsZ\n69SlQWI0SpymUIJ4KEo8FCMaioy0lARPlC++GF7+cu8k13G8W1+ft27sSXOpSWmDJ8aFzy/YWjFV\nsdiEadzzH/wAGTdL1s0x6Awx4N8c1ymWOUqYeCZPfDhHeDhNaHCI0NAQoYEhQkPD3m1waOQ2NOyt\nHxwiNDhM5MixUcskX2YsFhA51seaP/vw1N9nLaXTXgthIVNhJa1Gkz2u5P9McP0jj4x0l6weq4t9\naSfNY8ce40VbXlRcltf89MaNGmOMMQFTCZI+BfwU+I2q3iMipwCP1aZY8180HKU10cpjxx4j5+ZY\n0bxiSvtn81nSTprB7CDH08c5kT2Bn9GXaDhKLBybeitVKOR1E0skoLXVW1aYKNVxRgb6F26O42dX\nc8DJg5v37gtBwkQJAQqCV98rOZmcyNjyjX1eohwhIK5KXAQiCYhFIBJGwxFy4tIbgu4wSNgFyaCh\nHMlYI42JZpoSzSRijcSiCa+lolT3nODrB8f1BJ8XPt/ATbNZyGZRJweuWxyjVQjL1H+/xeX+56QC\nGvZOzEPvfQ+xT38WCXQDdBNx9l/5Rxw+lip2RQxJiFgoSmM4SSgypmtYFNwmKB/eVEgVyWRZ94Yr\niHYfGbfa6Wjj0GeuQQrfk+uCgmjheywEr4royOPCcm87fz8UCWyPes/zbh4nnyOvee91FESVeCjK\nsutuKNljVIH8+/7Cfx3vO1N39D0aeOw/L7WdBtYFn4/ed/TfbGMuV4uerFYX+/Yc2YPjOqPGIzmu\nQzxsLUnGGGNmZiqJG24Gbg48fwJ4Q+G5iPyVqv5tdYs3v0VCEdqT7TzZ9ySO67C6ZXXJLl6O6xS7\nzR3PHOd4+njxin9IQsQjcVrjrbXJ/hUKjUxoOh1jg5RCVjV1vRPPYLBQmBuo8LjwPBikQelgKTiH\nVCHbWzC9dTADW7CVpsy4EcGbOKZUzrhcPke/m+HoYD86MNKaFA/HSUaTxWDV9UML13/vLn6wo17L\nX/GfKm7IhRi40UA4Ivhp+0KImx99El2YSymQoU6dHJJ30YyDuC76gvUs2fYnrLnxZmI9R8l2LeHQ\nOy9j6PeeT3tGRl5EXZAskB35LAuf89hWwcIymFqLoQiaiHPkfe8qOaas50NXMnzeORMfowZcdTnh\nOrR/5wfEnx0fffEpgAAAIABJREFUvGWXdfK7t7wYGAlQC8ElhaWKF7D6AZ2ooELxb1xEvIyMqoiC\noP5z/6PTkfF+4meGLDw/7a0fIFYiqJwJq4tHlErakHetJckYY8zMVTP9z2XAovhhDgpJiI5kB8+c\neAbHdVjburbYSnQic4K+TB8Zxz+hVIhFYjREG+pnUHE1Bq4XBK+yF1oJCiftheBnFkTDUaLh6LgM\nhY7rkMlnRp3weg+8uzDedyYhf33wxHjsPlXibt7KU9uuCrS8uERGtcQUAlgdCVoL6wqBqut6rYX5\nMa1hwYC23BidQkDhL+t/4Xlw1ZV0fv0/iPQcxelawpE/fau3fOyEwqUm/53o85msW2OJRA6FlsRj\nV7yVZV/6KqFMduSzi8c4dsVbacuUeM1i0cYkaAjJ6McS7HbnPx/btbJUICoCAkev/RDLrvkMoeH0\n+DLUzqKpi1PdKZYkl7C8aXlxmYtb2Xx2xhhjzASqGSTNn+wAs0xEaE+20z3YTfdQd/EELBqOeq0T\nCZvMEBhpAarC5Ka1EAlF5m/a4LGtQNUWDLomCcL6r3gb/e98a+n5ooLlGxsQTeX5pNuOftp/6qmw\nbCmdX7ieyMHDOCuXc+Sa99P/+leP7FsykKlttdV/+WUQT7DkM1+CA4dq+loBi6YuHpu0AbyLF/P2\n/7Exxpi6Uc1fkjJpuBaHQqBkTF2qdRA2C/ovv8wLSuaZ/je+hu5LL+a+NeekJt+6KhZFXTyUG2Jv\n715euv6l49ZZkGSMMWamqnlGtGiuXpqZuXXPrVz0zYs44/+cwUXfvIhb99w610UypmZu3XMrr/jO\nK6CLzZNvXRWLoi7efWQ3rrrjJpEFC5KMMcbMXDWDpJsn38QsdrfuuZWP3v5RDvYfRFEO9h/ko7d/\n1AKlOjJfg9z5WK7C3/uhgVnrageLpC4uJG3Y1LVp1HJFLUgyxhgzYxX9kojIy4HVwC9U9cnA8nep\n6jcAVPWzZfa9BPh7IAzcqKqfK7PdG4DvA+ep6r1TeRNmflNVjgwdYW/vXj79P58m7YwexJ520nzm\n159hRfMKTmo9ic6Gztpk+jMzVjjpL3yHB/sPcu0vr6VnqIdLTr2EaMhLXx8NexP6RkKRWfkuS5Xr\no7d78yO95vTXVHQMVSXn5hjMDjLsDDOUG2IoN8RgbpCh3BDDufHLgstLLTsydKSY5r0aplsXL8R6\nONWdYmnjUpY1LSsuy7v5qU0kbYwxxpQxaZAkIp8FXgjcD3xERL6sql/xV78P+MYE+4aB64GXAgeA\ne0TkFlXdNWa7ZuCDwN3TehdmXlBVjg4f5bFjj7H36F729u5l77G97D26l75M34T79qX7uPwHlwPQ\nEG1gbetaTmo9iZNaT2Jt28jjpY1L7QRolh0bPsbO7p3s7NnJP937T+OC3Ew+w+e3f57Pb/98yf2j\nIS+bYCwUK84BVlhWCKaCgdXY+1LrYuHYqON86c4vlQy+P/mrT7L32F4Gc4HAJzsm+AkERMXJeCsQ\nC3uZKhujjTREG4q3tkRbcflNO2+a+gdexnTr4oVaD6e6U+NakfKatzmSjDHGVEUlLUmvAbaoqiMi\nnwD+XUROUdWrmLzv+/OAvf48HojId4FLgV1jtvs08Hngw1MpvJkblQZDLfEWTu04lZef+nLWd6xn\nQ8cGrvn5NTw7+Oy4Yy5tXMrfvORveKrvKZ46/hRPH3+aPUf38It9vxh14pqMJFnTusYLnlrXsq5t\nXTGgWta0jJDUb+KB+eDY8DFS3Sl29uwsBkYH+w9WtO9nXvIZcvkcOTdHLp8jm88WHweXl1qfdbMM\n54Y54Z4Yv++Y+7zmJy+Mrz/bz9fu/9qoIKYQwHQ2dI5blowmxy1riDYUlwefV9Kl69dP/7riz68C\n062LF1w9PJAdYF/vPl614VWjljuuQ1OsaY5KZYwxZiGpJEiKqKoDoKp9IvIa4AYRuZnSc3UGrQL2\nB54fAJ4f3EBEzgXWqOqPRKTsj7OIXAlcCbB27doKim1mqhAM7T3mBUGPHXus+LgvPT4Yetn6l3Hq\nklPZ0LGB9e3rS7b6fPjCD4/qFgWQiCS4+sKr+f2Tfh9OGl0Gx3U41H+Ip477wVPf0zx1/Cke732c\nO568g5ybK24bD8dZ27q2GDQFW6CWNy2fcG6qW/fcynV3Xceh/kOsaF7BtvO3VdxNq15NFhCd1HoS\n5yw/h8vPupxNSzexqWsTl3730pIn/SubV3LZmbOTWS7v5scFTm+8+Y10D3aP23ZF0wpuf8ftc9b6\nuO38beP+3mdgunVx1ephf9tiXbx6zeqpv4sq2N2zG0U5a+lZo5bbRLLGGGOqpZIg6XERuQjvSuR+\nVc0DV4jIZwjM8j4dIhICrgP+ZLJtVfUG4AaArVu3LooUtzNV6Yl/qWDo8WOP89ixx0YFQ82xZi8Y\nOsULhk5tP5VTO06dUhe4wutXGpBEQhHWtK5hTesaXsgLR63Lu3kODxzm6eNe4BRshfrN078hk88U\nt42GoqNaoE5qO6kYQN136D4+fsfHZzSeZb47OnSUVE+qGAzt7N45KpnAutZ1bFm+hbed9TY2Ld3E\nmV1n0hJvGXecUif9iUiCbedvm5X3ARAOhQmHwqNOhq++8OqS5fpfF/yvOe2eWfj7+dKdX+IQM07e\nUJO6eCr1MIyui7ecu2VO6uJi0oalo7vbOa5j3e2MMcZURSVB0mV4XTnuBoqX7VT1oyLyT5Ps+wyw\nJvB8tb+soBnYDNzhn8gsB24RkdfO90HD8125gez92X5OaT+lGBAVgqJywVChm9xUg6GJvOb011Ql\n+AiHwqxqWcWqllVcsOaCUetcdeke7ObJvieLQVShFequA3cx7AxPeOxCMomWeAvtyXbaE+20J9tp\njDbO+zFRR4aOsLN756ig6PDA4eL6da3rOHfFuWxeuplNXV5A1BxvrujYUw1yZ8t8LRd4Zbv4lIs5\n55oZz5M03bp4wdXDqe4UK5pW0NnQOW5dNBydgxIZY4xZaCYNklR1GEBE7heR81T1nsC6Z8rvCcA9\nwAYRORnvR/nNwFsD+x8Hir9yInIH8Jfz9Ye5nlx353VlB7IX1DoYmkshCbG8aTnLm5Zz/urzR61T\nVboHu4vB07W/vLbkMfrSfVz5X1eOWhYNRUcFTaPuSy1Lts+o+89krYGTBkRt69i6Ymuxu9xUAqJy\nqhXkVtt8LVe1zKAuXnD1cKo7VXJ+JLA5kowxxlTHVH5Nng9cLiJPAYN4VzRVVZ9Tbgd/gPH7gJ/i\npZ79hqruFJFPAfeq6i0zKLsJGM4Nk+pO8cDhB7j/0P0cHCg/WPwbr/3GggmGpkNEWNa0jGVNyzhv\n1Xlcf8/1JcfZLG1cylde8RV6h3vpTfeO3AceP3LkEXrTvRxPHy+b6rkh2kB7op22RNukAVZHsoPW\neCvRcLRka+BHfvkRfvb4z3DUYWf3zmISDEG8gGjlVjZ3bS52mbNB7AvSlOrihVYPn8ic4MnjT/KH\nG/+w5HoLkowxxlTDVH5NXj6dF1DVHwM/HrPsY2W2ffF0XmMxOjxwmPsP3c8Dhx/ggUMPsPvI7mIW\nuJPbTiYZSZbsUrayeSUvWPuC2S7uvFZunM3VF17NOcvPqegYeTfP8czx8QFViQDrqb6n6E33MpAd\nKHu8lngLg9nBcZncsvksP3viZ5zcdjLnrTqPs5aexaauTWzs2mgB0RzKu3lcdXHVJa8jj111UZR8\nPg9UbcKkKdfFC6ke3tmzE8BakowxxtRUxb8mqvpULQtiysvlczxy5BEvIPKDosKg+0QkwXOWPocr\ntlzBluVbOHv52XQkO8a1QhS2nc0B9vWiGuNZwqEwHckOOpIdFe+TzWdLtk71DvfSl+7j2w99u+R+\ngvCTt/2k4tcxlVHVUQFO3s17AY4fAAkC4m0XfAzeOJhIKEIsFCMZThbncoqEIkRCES+zokNukiJU\nWs5FXRcXkzaMmSNJVRERwlI+i6UxxhhTKbvkNg/1Dvfy4OEHiwHRQ90PFYOdFU0r2LJiC+9a/i62\nLN/CGZ1nlByoPJ8Hss9HczGeJRaOFbv9lfKLfb8o2Q1wRfOKWhetrhUCnFItO4qCUgxyALxFSkhC\nxMIxwuJlzotIpDj5bSHQCUuYkITGPa6QZeWsglR3itUtq2lPto9aXphIdjF2ITbGGFN9FiTNMVdd\nnuh9ggcOPcD9h+/ngUMPsK9vH+B1G9nYuZE3bXoTW5ZvYcvyLVM6QV7oA9kXuvmQbns+UlUc1xl1\nE5FisBMJecFNNOS37vhBTuG+VJATlrCdXNeJckkb8m7e0n8bY4ypGguSZtlgdpCHuh/igUNe17kH\nDz/IicwJANoSbWxZvoXXb3w9W5ZvYfPSzSSjyTkusZkri7k10FUXx3XI5XM4roOqjiTGEEiEEzRE\nG0hGkzREG4iGosXAaAotO6bO9A73cuDEAd68+c3j1jmuY+PyjDHGVI0FSVVSKlXzq097Nc/0P1MM\niB44/ACPHHkEV10ANnRs4JL1l7BlhddKtK5t3aK5mp3NZxnODSMixXEbhav8ZsRCbg0MtgTl8qOH\n64QlTDKapDXeSmOskXgkPioQWiz/T8xoEyVtyGt+Run2jTHGmCALkqqgVKrmq39+NZ+845P05/oB\nLw302cvO5j1b3+MlWFh2Nq2J1rks9qwrBEauuiSiCda0riHv5hl2hknn0vRn+lG0ODBeRQkRKnaf\nCkvYWgnqiKqSc3PFIKhwcaDw3cZDcRpiDSQjfmuQHwAVkiAYM1a5pA3gd7eLWHc7Y4wx1WFnIlVw\n3V3jJ2511cVRh4+96GOcu/xcNizZsChP/HL5HIO5QS8wCidY3bKatkRb2W6EeTdPzs0Vu1mlnTTD\nuWHS+TRDuaHi+JNghrFIKFLsZrUYP+O5lHfzXhDkf2dAcXyQiJCMJGmONdMQbSARSYwKhKzV0ExV\nqjvFutZ1tMRbxq0TxP7/G2OMqRr7RamCQ/2HSi5PO2kuP+vyWS7N3HNcx5vjx/W6v6xuXk1bso2G\naMOk+4ZDXmtRuW4zwZNyx3XIOJliS9SwM0y/24+ooGixS1awO58N0J8+V10yToaMkylmhouGoiQj\nSVriLTREG4iFY6MywhlTTanuFM9d8dyy6+1vzhhjTLXYL0oVrGhesehTNefdPIO5QRzXIR6Os6J5\nBe2JdhqiDVUNSgpBVJzS3WrGDvgvdPEbdoZJO2kG8t4ErsWWKP+YhRTPNt5lRDafJZvPksvnEBFC\nEqI13srK5pUko0kvTbadlJpZcmToCIcGDpWdRBawOZKMMcZUjZ3hVMG287fx4f/+8Ej2LRZHqua8\nmy92gYuEIixrXEZ7sp3GaOOcBRqFuW5i4VjJ9YX00cEufcPOcDGQOp45XkwlLQiRsJc6eqEHT8FW\nokIrXCKSoDPZSUuihUQkYXPQmDm1s7t80gYYSf9ujDHGVIP9olTBlhVbUJSWeAv9mf4FnarZVZeh\n3BC5fI5wKExXQxcdyQ6aYk11cQItIt64mHAUxs/Bi6oWW1DSTpqB7AAD2QH6Mn1ewgH1AohoKFrs\nWlaPyrUSrWhaQUOswVqJzLzzcPfDCMLGro3j1rnqWmIXY4wxVWVnQVVw5/47AfiPN/wHp3acOsel\nqb5gYBSSEF2NXSxJLqEx1rjgBt+LCPFInHgkTnO8ma7GLmAkeMrkvdaWE5kTDOQGGBge8IInlJCE\n5uV4HGslMgtBqjvFye0nl5wLyXEdy2xnjDGmqubPmVwd275/O0sbl7K+ff1cF6VqVJWh3BDZfBYR\noTPZSWdjJ02xpgUXGFUiGDwRpxg8uep6wZOTIe2k6c/2M5gdpD/TX9y30AVwtlJbl2slWt60nMZY\no7USmbqU6k5xwZoLSq7Lu3mbeNsYY0xV2ZnSDLnqcueBO3nxSS+u+yvxqsqwM+xlLwOWNCyhq6GL\npliTdWMpIyQhEpEEiUiCVlpZxjLAO2krtDwN54aL3fb6817wJAihUKg43mm6n2+hlaiQgt5aicxC\n9OzAs/QM9ZQdj5TXPPGwtSQZY4ypHguSZmhXzy760n1cuPbCuS7KtKgqaSdN2kkjIrQn2lnXto6m\nWJO1NsxAOBQmGUqSjCZpS7QVlxcy7mWcDEO5oWLwlHfzXuIPgYhEii1PY1vtgq1E4AVprQlrJTIL\n286eiZM2OK5TdtoAY4wxZjrsbGqGduzfAcCFq+srSCpM0grQlmhjbetamuPNdoJdY4X5mhqiDbQn\n24vLc/lcseVpMDvotTpl+lEUVS9rYqHLX2eyk+Z4M8lo0lqJzKKQ6k4RkhAbO8cnbQDvYk+5jJbG\nGGPMdNgZ8Qzt2L+D05acVhyjMp8VWi8AWuItrOpYRUu8pW4ztC0khYx7jTTSkewoLi+0HAHWSmQW\nrVR3ilPbTy07IbWI2P8NY4wxVWW/KjOQdtLcd+g+3rr5rXNdlJLybp60kyabz6KqNMebWd++npZE\ni111rRMTzflkzGKgqqS6U7zopBdNuI0FScYYY6rJflVm4N6D95LNZ+fNeKRcPkcmn/GymvkTobbF\n22hLttEYbbQUucaYunN44DBHh4+WHY8E1pJkjDGm+uxXZQa2799ONBTlvJXnzcnrFxIAFAb9x8Nx\nliSX0JpoJRlJWlBkjKl7qe4UUD5pA4BiLUnGGGOqy35VZuDO/XeyZcWWsv3kq6kwmWnaSeOqC0BD\ntIFlTctojnmD+K1bljFmoUl1p4iEIpzeeXrJ9Xk3TywUswQmxhhjqsqCpGk6OnSU3Ud2c9X5V9Xk\n+KpKJj8y/42q0hRrYlXzqmJmM7tyaoxZ6FLdKTZ0bCib4juveWs1N8YYU3V2lj1Ndx64E4AL11Rn\nPFKpSUFbYi0sa11GY6yRZCRpE7oaYxaVQtKGl65/adltHNehOdY8i6UyxhizGFiQNE3b92+nNd7K\npq5N09q/mHnOzSIqhEIhWuOtrGxeSUO0gWQ0OW4iUWOMWUwO9B+gL9M34XikvJu3iWSNMcZUnQVJ\n06Cq3Ln/Ts5ffX7FrTuO65B20uTyOcCbVLQt0UZ7sp1kJEkikrA+9cYYE1BJ0gbHdWw8pjHGmKqr\neZAkIpcAfw+EgRtV9XNj1m8D/hRwgB7gXar6VK3LNRP7+vZxaOAQ79n6nrLbFDLPOa4DQDwcpyPZ\nQWu8lWQ0STwct6DIGDMr6rUeTnWniIainLbktLLbiIgFScYYY6qupkGSiISB64GXAgeAe0TkFlXd\nFdjsAWCrqg6JyJ8DXwD+qJblmqkd+3cA48cjOa5Df6YfgEQ0wdLGpbTEWyzznDFmztRzPZzqTnF6\n5+mT1p82XtMYY0y11bol6XnAXlV9AkBEvgtcChR/nFX19sD2dwFvq3GZZmz7/u2sblnN2ta1o5YP\n54ZZ3rSclc0riYajc1Q6Y4wZpS7rYVdddnbv5FWnvWrSbS3TpzHGmGqrdWaAVcD+wPMD/rJyrgBu\nK7VCRK4UkXtF5N6enp4qFnFqHNfh7gN384I1Lyi5rjnebAGSMWY+qVo9DKPr4iNHjlSpiOM9ffxp\n+rP9E45HAm+MqAVJxhhjqm3epE8TkbcBW4Evllqvqjeo6lZV3drV1TW7hQt46NmHGMwNlk39bT/W\nxph6NVk9DKPr4s7OzpqVpZC04aylZ5XdRlUJScjqXWOMMVVX61+WZ4A1geer/WWjiMjFwLXAi1Q1\nU+MyzciO/TsQhPNXnz9unYgQDVkrkjFmXqnLejjVnSIejrO+fX3ZbSyznTHGmFqpdUvSPcAGETlZ\nRGLAm4FbghuIyBbgn4HXqmp3jcszY9v3b2fT0k20JdrGrbNuH8aYeagu6+FUd4ozOs+YsPuyqy7x\ncHwWS2WMMWaxqGmQpKoO8D7gp8Bu4HuqulNEPiUir/U3+yLQBNwsIg+KyC1lDjfnBrID/O7w70qO\nR1JVRMSCJGPMvFKP9bCrLjt7dk46Hinn5ohHLEgyxhhTfTU/o1fVHwM/HrPsY4HHF9e6DNXy22d+\nS17zJccjOa5DPGJzHxlj5p96q4f39e1jKDc0aZDkqksikpilUhljjFlM5k3ihnqwY/8OEpEE5644\nd9y6vOZJRpJzUCpjjFlYCkkbJguS8m7eWpKMMcbUhAVJU7B9/3a2rtxacqCw4zokwnZF0xhjZirV\nnSIZSXJK+ykTbidYF2djjDG1YUFShQ4PHOaJ3idKjkcCL0hKRq0lyRhjZirVnWJj18aKAiALkowx\nxtSCBUkV2rF/B0DZ+ZEAS0VrjDEz5LgOu3t2T9rVrsCCJGOMMbVgQVKFtu/fTmdDJ6cvOb3kekv/\nbYwxM/dE7xMMO8MVBUmK1bvGGGNqw4KkCrjqcuf+O7lg9QVls9cJMuF8HsYYYyZXadIGV13CEiYk\n9jNmjDGm+uzXpQKPHn2Uo8NHJ+xqZ1c0jTFm5lLdKRqiDZzcdvKE2zmuY+m/jTHG1IwFSRUojEcq\nl7Qh7+aJhWN2RdMYY2Yo1Z1ic9fmSetTS/9tjDGmluysvgLb929nfft6ljUtK7m+MJGsMcaY6cvl\nc+w+UlnShrzmiYet3jXGGFMbFiRNIuNkuPfgvRN2tXNcxyaSNcaYGdp7bC/ZfLaiIMm62xljjKkl\nC5Imcf/h+0k76cmDJJsjyRhjZqTSpA0AKJYsxxhjTM1YkDSJO/ffSSQU4Xmrnld2GxfXun0YY8wM\nPdz9MM2xZta2rp10W0uWY4wxppYsSJrE9v3bOXvZ2TTFmspuIyr2Y22MMTOU6k6xeenmslMtjGX1\nrjHGmFqxIGkCvcO97OzeOWFXuwLr9mGMMdOXzWd59OijlXW181mQZIwxplYsSJrAXc/chaJlU38X\nCURDFiQZY8x07Tmyh5ybqzxIEguSjDHG1I4FSRO4c/+dNMWaOGvZWWW3cdVFEMKh8CyWzBhjFpZU\nT+VJG/JunlgoVnG3PGOMMWaqLEiawPb923n+qudPeLXS0n8bY8zMpbpTtCXaWNW8atJt82oTyRpj\njKktC5LKePr40xw4cWDSrnY2kawxxsxcqjvF5q7KkjY4rmMZRY0xxtSUBUllbN+/HWDSpA15N09D\ntGE2imSMMQtS2kmz99jeiscj5d28TSRrjDGmpixIKmPH0ztY0bSCdW3rJtzOZn03xpiZ2XNkD47r\nVBwkWb1rjDGm1ixIKiHv5rnrmbu4cM2Fk3b9EMTSfxtjzAykuitP2gAgYnPTGWOMqS0LkkrY2bOT\nE5kTk6f+xmZ9N8aYmUp1p1iSXMLypuUV72P1rjHGmFqyIKmEwnikC9ZcMOm2ImJzJBljzAykulNs\nXlpZ0oYCC5KMMcbUkgVJJex4egdndp1JR7Jjwu1UFbAfa2OMma6h3BB7eytP2lBg9a4xxphasiBp\njKHcEA8cfoALVk/eipTXPPFw3CY0NMaYadp9ZDeuuhUHSapqE3gbY4ypuZoHSSJyiYjsEZG9InJN\nifVxEbnJX3+3iKyrdZkmcs/Be8i5uYrGI1mGJWNMPZjP9XAhacOmrk0Vbe+4DrFIrJZFMsYYY2ob\nJIlIGLgeeAVwJvAWETlzzGZXAL2qeirwd8Dna1mmyex4egexcIznrnzupNtakGSMme/mez2c6k6x\ntHEpy5qWVbR9XvMkwlbvGmOMqa1atyQ9D9irqk+oahb4LnDpmG0uBb7pP/4+8Acyh/3XduzfwdaV\nWysKfhzXsYlkjTHz3byuhwtJGyrluA7xSLyGJTLGGGNqHyStAvYHnh/wl5XcRlUd4DiwpMblKqln\nsIdHjz1a0Xgk8PrG2xxJxph5bt7WwwPZAfb17ptSkJR389aCb4wxpubqJnGDiFwpIveKyL09PT01\neY0dB3YAVDQeyS+Tpf82xiwqwbr4yJEjMzrWrp5dKDqlIMlVl1jYxiQZY4yprVoHSc8AawLPV/vL\nSm4jIhGgFTg69kCqeoOqblXVrV1dXTUp7I6nd9CeaGdj18aKtle1iWSNMfNe1ephGF0Xd3Z2zqhg\nhaQNm7ss/bcxxpj5pdZB0j3ABhE5WURiwJuBW8ZscwvwDv/xG4FfamEColmkqmzfv50L1lxASCr/\nWKy7nTFmnpu39XCqO8XK5pUsaZhazz4LkowxxtRaTX9pVNURkfcBPwXCwDdUdaeIfAq4V1VvAb4O\nfFtE9gLH8H7AZ93jvY/TM9TDhWsurGj7vJsnEopMKaAyxpjZNp/r4VR3asqtSIJYkGSMMabmav5L\no6o/Bn48ZtnHAo/TwGW1Lsdktu/fDsCFqysMkjRPMpqsZZGMMaYq5mM9fDx9nKeOP8UbNr5hSvsp\n1s3ZGGNM7VkziG/H0ztY17qOVS1jkz6VlsvnbK4OY4yZpl09uwCmnLTBWvCNMcbMBvulAbL5LL89\n+FsuXFtZKxJYS5IxxszEw90PA7Bp6aaK93Fch3jY5kgyxhhTexYkAQ89+xBDuaGKU3+DP+u7zdVh\njDHTkupOsbplNW2Jtor3ybt5m0jWGGPMrJA5SCQ3YyLST4z9uOTnrBAhwuRIo7jT2LsTmNkEI3Ov\n3t+DlX/u1ft7mGr5T1LV2sxfMEdEZIAYT89aXSyEcHHIk6vSERfb3+B8VO/vwco/9xZ9XWxqo15H\nv+7RjG6d60JMl4jcq1q/5Yf6fw9W/rlX7++h3stfJY9YXTx36r38UP/vwco/9xbCezDzk3W3M8YY\nY4wxxpgAC5KMMcYYY4wxJqBeg6Qb5roAM1Tv5Yf6fw9W/rlX7++h3stfDfX+GVj55169vwcr/9xb\nCO/BzEN1mbjBGGOMMcYYY2qlXluSjDHGGGOMMaYmLEgyxhhjjDHGmIC6CpJE5BIR2SMie0Xkmrku\nT6VE5EkReVhEHhSRe/1lHSLy3yLymH/fPtflLBCRb4hIt4ikAstKllc8/+B/Jw+JyLlzV/IRZd7D\nJ0TkGf97eFBEXhlY91f+e9gjIi+fm1KPEJE1InK7iOwSkZ0i8kF/eV18DxOUvy6+AxFJiMhvReR3\nfvk/6S9cmiLDAAAgAElEQVQ/WUTu9st5k4jE/OVx//lef/26uSx/rdVjXVxv9TDUf11s9fC8+A6s\nLjZmulS1Lm5AGHgcOAWIAb8DzpzrclVY9ieBzjHLvgBc4z++Bvj8XJczULbfB84FUpOVF3glcBsg\nwPnA3XNd/gnewyeAvyyx7Zn+31McONn/OwvPcflXAOf6j5uBR/1y1sX3MEH56+I78D/HJv9xFLjb\n/1y/B7zZX/5V4M/9x38BfNV//Gbgprn8/Gv82dRlXVxv9bBfprqui60enhffgdXFc/wd2K1+b/XU\nkvQ8YK+qPqGqWeC7wKVzXKaZuBT4pv/4m8Dr5rAso6jq/wDHxiwuV95LgW+p5y6gTURWzE5Jyyvz\nHsq5FPiuqmZUdR+wF+/vbc6o6iFVvd9/3A/sBlZRJ9/DBOUvZ159B/7nOOA/jfo3BV4CfN9fPvbz\nL3wv3wf+QERkloo72xZSXTxv62Go/7rY6uF58R1YXbxw62JTY/UUJK0C9geeH2Di/+jziQI/E5H7\nRORKf9kyVT3kPz4MLJubolWsXHnr7Xt5n98N4huBrjXz+j343QW24F1Bq7vvYUz5oU6+AxEJi8iD\nQDfw33hXVPtU1fE3CZaxWH5//XFgyeyWeNbMu++qQguhHoY6rANKqIs6IKje62GwutiYqaqnIKme\nvVBVzwVeAbxXRH4/uFJVFe8HvC7UW3kD/glYD5wDHAK+NLfFmZyINAH/F/iQqp4IrquH76FE+evm\nO1DVvKqeA6zGu5J6xhwXyczMgqqHoT7LTB3VAQX1Xg+D1cXGTEc9BUnPAGsCz1f7y+Y9VX3Gv+8G\n/h/ef/JnC83w/n333JWwIuXKWzffi6o+61e2LvA1RroQzMv3ICJRvB+176jqD/zFdfM9lCp/vX0H\nAKraB9wOXIDXfSbirwqWsVh+f30rcHSWizpb5u13NZEFUg9DHdUBpdRbHVDv9TBYXczCrYtNjdVT\nkHQPsMHPaBLDG5B3yxyXaVIi0igizYXHwMuAFF7Z3+Fv9g7gh3NTwoqVK+8twB/7WX3OB44HuiHM\nK2P6hv8h3vcA3nt4s58V52RgA/Db2S5fkN+H+uvAblW9LrCqLr6HcuWvl+9ARLpEpM1/nAReiteX\n/3bgjf5mYz//wvfyRuCX/hXmhaju6uIFVA9DndQB5dRLHQD1Xw+D1cUs7LrY1NrYTA7z+YaXOeZR\nvP6o1851eSos8yl4mWJ+B+wslBuvj+wvgMeAnwMdc13WQJn/A6/5PYfX1/eKcuXFyzxzvf+dPAxs\nnevyT/Aevu2X8SG8inRFYPtr/fewB3jFPCj/C/G6cDwEPOjfXlkv38ME5a+L7wB4DvCAX84U8DF/\n+Sl4Jwx7gZuBuL884T/f668/Za7/hmr8+dRVXVyP9bBfvrqui60enhffgdXFc/wd2K1+b6JqAbYx\nxhhjjDHGFNRTdztjjDHGGGOMqTkLkowxxhhjjDEmwIIkY4wxxhhjjAmwIMkYY4wxxhhjAixIMsYY\nY4wxxpgAC5LMKCKiIvKlwPO/FJFPVOnY/yoib5x8yxm/zmUisltEbg8sO0tEHvRvx0Rkn//451M8\n9k8L861MsM3fiMhF0y3/mGMdEJGHReQhEfmJiCytQvneJSLLq1E+Y0xtWF086bGtLjbG1JQFSWas\nDPB6Eemc64IEBWbWrsQVwLtVtfjjqKoPq+o5qnoO3pwQH/afXzyV11HVl6tq/yTbXKuqt0+0zRT9\nnqo+B2+eiGtmWj7gXYD9MBszv1ldPAGri40xtWZBkhnLAW4Arhq7YuzVRxEZ8O9fLCK/EpEfisgT\nIvI5EblcRH7rX3lbHzjMxSJyr4g8KiKv9vcPi8gXReQe/yrdnwWO+2sRuQXYVaI8b/GPnxKRz/vL\nPoY3ed7XReSLlbxhEblYRO4Qkf/Cm1wPEblVRO4TkZ0i8qeBbQ+ISJuInOq/7tf9bW4TkYS/zb+J\nyOsC239CRB7w39tp/vKlIvILf99/FpFnCrOKT+B/gFP9/d8WeO+frbR8IvJHwDnATf7V25j/2e/y\ny/f5Sj4zY0zNWV2M1cXGmLljQZIp5XrgchFpncI+ZwPvATYCbwdOU9XnATcC7w9stw54HvAq4Kv+\nj9kVwHFVPQ84D3i3iJzsb38u8EFVPS34YiKyEvg88BK8H5rzROR1qvop4F7gclX98BTKvxX4C1Xd\n6D9/h6o+1y/PNhFpL7HP6cCXVXUTMAy8rsyxn1XVLXifxTZ/2aeAn/j73gqsnKhwIiLAq4GHRWQ1\n8BngImAL8ILCSc5k5VPVm/BmXP8j/0puO97s65v8K6R/O1E5jDGzyupiq4uNMXPEgiQzjqqeAL4F\nfGAKu92jqodUNQM8DvzMX/4w3o9xwfdU1VXVx4AngDOAlwF/LCIPAncDS4AN/va/VdV9JV7vPOAO\nVe1RVQf4DvD7UyjvWHeq6tOB51eJyO+AO4HVwPoS++xV1Yf9x/cx+n0G/aDENi8Evgugqv8FTNQt\n49d4P6ZJvJOR5wO/VNUjqpoD/p3S772S8h0DXOBrIvKHwOAE5TDGzCKriwGri40xc2QqfYvN4vJl\n4H7gXwLLHPzAWkRCQCywLhN47Aaeu4z+O9Mxr6OAAO9X1Z8GV4jIi5m9H4ri64jIxXg/dOer6rCI\n/AZIlNgn+J7zlP//lKlgm4n8nqr2BcpX6X6Tlk9VcyKyFXgpcBnw53gnSsaY+cHqYquLjTFzwFqS\nTEmqegz4Hl73i4Ingef6j18LRKdx6MtEJOT3jT8F2AP8FPhzEYkCiMhpItI4yXF+C7xIRDpFJAy8\nBfjVNMpTSitwzP9R3oR3pbTatgNvAhCRVwITZkEa427gIhFZIt7g5jcztffeX3g98bIvtfhXUK/C\n6zJijJknrC62utgYMzesJclM5EvA+wLPvwb80O/68BOmd2Xxabwf1RbgPaqaFpEb8bof3O/39+6h\nfJ9yAFT1kIhcA9yOd/XzR6r6w2mUp5QfAVeKyC68E4e7q3TcoI8D/y4i7wR+A3RT4eepqgdE5K+B\nO/De+62q+qMpvPa/ADeKyDDeCdb3RSSOd9Fk24R7GmPmgtXFVhcbY2aZqI5tcTfG1Jo/SNpRVUdE\nXog3qHfrXJfLGGMWE6uLjTHlWEuSMXNjHfAffveUDPBnc1scY4xZlNZhdbExpgRrSTLGGGOMMcaY\nAEvcYIwxxhhjjDEBFiQZY4wxxhhjTIAFScYYY4wxxhgTYEGSMcYYY4wxxgRYkGSMMcYYY4wxARYk\nGWOMMcYYY0yABUnGGGOMMcYYE2BBkjHGGGOMMcYEWJBkjDHGGGOMMQEWJBljjDHGGGNMgAVJZtEQ\nkReLyIEaHXudiKiIRGpxfGOMqVdW9xpj6pEFScZMg4g8KSIXz9FrXywi94vIoIgcEJE3zUU5jDFm\nts1V3SsibxKRHSIyJCJ3lFh/jojc56+/T0TOme0yGmOqy4IkY+qIiJwJ/DtwLdAKnA3cN6eFMsaY\nhe8Y8GXgc2NXiEgM+CHwb0A78E3gh/5yY0ydsiDJ1Ix/xe/DIvKQ3+rxdRFZJiK3iUi/iPxcRNoD\n298sIodF5LiI/I+IbPKXx0TkQRF5v/88LCLbReRjk7x+UkT+VUR6RWQXcN6Y9StF5P+KSI+I7BOR\nDwTWfUJEvi8iN/llvV9EzvbXfRtYC9wqIgMicnXgsJeLyNMickRErp3pZ1jCR4F/VtXbVNVR1aOq\n+ngNXscYU6es7q1+3auqP1fV7wEHS6x+MRABvqyqGVX9B0CAl1S7HMaY2WNBkqm1NwAvBU4DXgPc\nBnwE6ML7+/tAYNvbgA3AUuB+4DsAqpoF3gZ8SkQ2AtcAYeBvJnntjwPr/dvLgXcUVohICLgV+B2w\nCvgD4EMi8vLA/pcCNwMdeK03/ykiUVV9O/A08BpVbVLVLwT2eSFwun+8j/nlHUdErhGRvnK3Cd7T\n+f7+D4vIIRH5NxHpmORzMMYsPlb3ljCDuncim4CHVFUDyx7ylxtj6pQFSabWvqKqz6rqM8CvgbtV\n9QFVTQP/D9hS2FBVv6Gq/aqaAT4BnC0irf66FPAZ4D+BvwTerqr5SV77TcDfqOoxVd0P/ENg3XlA\nl6p+SlWzqvoE8DXgzYFt7lPV76tqDrgOSOAHKRP4pKoOq+rv8E4Czi61kap+TlXbyt0mOP5q4O14\nJ0AbgCTwlUnKZIxZfKzuLWEGde9EmoDjY5YdB5qneTxjzDxgQZKptWcDj4dLPG+CYjeOz4nI4yJy\nAnjS36YzsP03gZOAH6vqYxW89kpgf+D5U4HHJwErx1xB/AiwLLBNcV9VdYED/jEncjjweAj//VXR\nMPAvqvqoqg4AnwVeWeXXMMbUP6t7Z88A0DJmWQvQP4tlMMZUmQVJZr54K14Xi4vxEhKs85dLYJt/\nBP4LeLmIvLCCYx4C1gSerw083g/sG3MVsVlVgwFHcV+/i8hqRvqjB7tVTJmIfMTvU1/yNsGuD415\n7RmVwxiz6FndW1ndO5GdwHNEJPiZPcdfboypUxYkmfmiGcgAR4EGvBaSIhF5O/Bc4E/w+tJ/U0Qm\nu1L4PeCvRKRdRFYD7w+s+y3QLyL/2x9kHBaRzSISHGD8XBF5vXjzb3zIL99d/rpngVOm80YBVPWz\nfp/6krcJdv0X4J0icoqINOCNEfiv6ZbDGLPoWd1bQd3rlzOBl6AhJCIJEYn6q+8A8sAHRCQuIu/z\nl/9yuuU0xsw9C5LMfPEtvC4ZzwC7GPlBRETW4qVe/WNVHVDVfwfuBf5ukmN+0j/mPuBnwLcLK/w+\n9a8GzvHXHwFuxLuS+v/Zu/PoSM7yXvzfp6pXSa19GUmzama8zoxjI2wnIYDBiU0SY5+EsJMN7FwC\nhMSQCyQEiAPEmMtyfycOgeRHlnucEHIvSQzx9VzgQgIkgMc4VosxNjMeZjybWuuMeu+ueu8fpSpV\nb1K31KXuVn8/5/Tp7qrq1iuN/VY99b7v89j+GcCrACzCWgf0Cytz5AHgjwG8d2W6yDs38gtvhFLq\ns7D+Vt+B9btlULgAm4ioFux7q/MGWNMUPwXgp1Ze/zngJLi4C8AvA1gC8OsA7lrZTkQtSgqTsRAR\nYKWhBXBAKfX6RreFiKhdsO8lombBkSQiIiIiIiIXBknU0sQqjlhuAe7vNbptRETbFfteItruON2O\niIiIiIjIhSNJRERERERELr5GN2AjBgcH1d69exvdDCKiqj3++ONzSqmhRrejntgXE1Gr2Y59MXmj\nJYOkvXv34tixY41uBhFR1UTkdKPbUG/si4mo1WzHvpi8wel2RERERERELgySiIiIiIiIXBgkERER\nERERuXgaJInIZ0UkJiLTFfaLiPx/InJCRKZE5AYv20NE1I7YFxMREdXG65GkvwJw+xr7Xwbg4Mrj\nHgCfqupbo1FA04C9e4GHHtpkE4mItr2/Qjv0xQ89ZLWlmdoEsF2t2h4iamueZrdTSv2biOxd45A7\nAfyNsirafltEekVkVCl1Yc0vzmat59OngbvvBgwD+OVfrk+jN+qhh4Df/33gzBlg927gQx8CXve6\nxraJiAhb1Be/6U3A2bPAz/98fRpdqy99CfjAB4B0unybRFaPdb+u9X2tn334YevckEqttuvuu4HF\nReAVr7ACgnIPXS/dVvzdm/HQQ8A99wDJ5Gq77rnHet2Ic1eztcetWc/vbNeG2nQEONzYxlCrEOuc\n6OEPsE7MX1JKHSqz70sA7ldKfXPl/VcBvEsptWZO2UmR0gP8fqCzE+joWH3u6irc1tlpbXPvc28v\n3mY/AoG1T07FnTtg/YzPfKbxHQMRNQUReVwpNdnAn78XW9EXk7cqBVMilQOuco8f/QjI50u/3+8H\njhyxvk/E+n77dXHAttH39nfa7wHg858H4vHS9kQiwJvfbH3G57Oe7Yf93v7OzTwqfcejjxYG3wAQ\nDgMPPAD84i8Wfrb438S9rZ4BLtC81x3N2K6iNk0COKZUnf9BaDtqmTpJInIPrGkgeF65A371V4FE\nwnqkUquPpSXrf4xUyurkUikgk6nth+u61Snaj46O1Uc4DHzzm6t3CW3JJPD2t1sdfE8P0NsL9PUB\n/f1W0OU+eXilGe/mEFFLW7cv/uQnt7Q9jt/+7cr7PvGJ0m32DcLiG4Vrva/lWPv9e95TuV1/8AfW\nMYZhPZum9Sh+X/yw9xlG4fvi/e73xceeOFG+TbmcdY4yTeu9+3P2w/5eoPA7a9lf/LpcgAQAy8vA\nxz9e+J2NlkoBb3ub9aiWHXBWO2roDibdQZf9/plnrH8rt2QS+PVfBx58cDUocwe57qAXKA1i7cDV\n/Vzrtr//+8IAyW7Xm98MfO976wfv5f4WlbYV/20qfeZ3fqe0TURVaPRI0qcBfF0p9Xcr758G8OL1\npniU3L3cs8e6K2Zzn2jsDt5+zuetKSKXLlmd7+XL1vPystVJJ5NWMJVIrAZV7kcms7o9nbYeJ0/W\n9kcJhazgyR7BikSA7u7V556ewsCqr896bT96eqw7aOU6GFsz3s0hamNNPpLkTV+8lfbutaZoFWtk\nm4Ctb1dx8Od+dr8+eNC6gVZs1y7g6acLP1v83eXeb3bfkSPW1MhiO3cCTzxRGBgahhUgmKb1bG/L\n51f3uZ/tffY1gP0o3ue+TrCvI373d0vbZHvXuwqDyOLrDXfA6352B7DF792/Z6XgVyng3/+9cruu\nu640SHW/LvcoPq6Wz7ofly5VbpffX/h7NAhHkqhajR5JehjAW0XkcwBuAnBp3TnwxTo6rBESNztg\n8K3x642Pl99e3HG5n+2ONZu1nnM56/lnfga4UKbZAwPAH/1RYQCWSFiv7Wf7ceHCarBmz/OvRKRw\niqA9TbCrywqyurutqQvl7ua8+93AHXeUTluo91QAImol3vTFW+lDHyp/Y6iRbQK2vl12X75en/7h\nD5dv1x//sTVDYqvdf3/59tx/PzA4uPXtsf3Jn1QOcu+/33pdHIhWCkyreb/esXaA8bznAefOlbZr\nfBz44hcLP1+s0r5Ko6vr7XNvv+UW4Pz50v1jY8BXv1rahnKjoe7AsThQLA4ey43EuoM+wwB+67eA\n+fny7SZag6dBkoj8HYAXAxgUkbMA3g/ADwBKqT8D8AiAnwVwAkASwK9V9cWBgBWgeDGFrJoAq9hH\nP1raudtzll/xitX/4e3gKpu1trmfTXP1pJbNFgZQy8urAZb7YQdf8bi1CPjMmdVt5eaaA9adugMH\nrABucHD1eWgIGBmxOrKxMauj7eoqDabseeAMqohaRkv2xbWyf3azTTFmu1qzPbZqgtxqA9N6+shH\nyrfrIx+xRgMb5YEHyrfrgQeAq65qTJuUKm0TURU8n27nhcnJSXXsWJMtF97s+p9yQ/Xu6YJ2cGWP\nXrmfDaOwc1YKuPNOYGam9Od0dVkjX3NzwOys9ZifL393qLvbCqDsYModWA0PAzt2WAFVb681hTAQ\nsAKocoFV8VTAZjsREnms0dPtvNCUfTFRvTXrOYvt2lCbrlMq+6RSwcY2iFoBg6TtoFyA9Xd/Zw0x\nuxNKhELA+94H3Hrr6nxgESvYWly0klwsLlpB08KCFUC5g6lYrHShqP295QKpwUErUYU9UjU8DHzl\nK1YbijMFffKTwKteVX5BaLnFol5kCmq2Tp22FQZJRESNtx37YvJGo9ckUT2IlE4PfNObrOCj0oW/\ne/GqPUrlTkyRyRQGUoAVjCWTVjC1sLAaSNnBVCwGnDoFfPe75TMV+XzlMxSlUsC99wLf+Y41GlXp\n4fevvg4GreAsHLaeQyFrSD8Uso6zp0y607oWp6K1nz//eeAtbymsY9Is9TmIiIiIaMsxSNrOXve6\nyhf59rS4tWwkkLK/O5ezRqUWF1cDqdlZ4NOfLv+zEgngs5+t/Xes9LsFg+UDLL+/dN83vlE+hftb\n3mIFmO4EGXZyDDtZhr1GqzgoWy9darlRsnKacYSrGdtEREREVEcMkqiyzQZS4bA17W5iwjpWBPjn\nfwYuXiz9ntFR4OjR1XVW2ezqGqxMpvQ5k7H228/2w719rX2XL6++Lg6QbJcuAb/3e5V/d01brZXl\nrp9V7n1npzXK1dm5us99nLvwsR1w/cu/FE5NPH0auPtua9TurrvKt6dYpQXFay00Xut7vvAFa9TP\nPep2991WkGtPl6z0cAeExY96aNbgbaVdz6tQVoiIiIiaD4Mk2pxaA6n77rOK7BavlXr7263pfJpm\njfT4/VYA4a7NUKw4WUXxNnt7pW32Z+66q3zgNjQEfOpTqzWy7PpYdnZBO+NgMrn6SCSs6YjnzhXu\nq3btn8+3GkDNz5dmKUylrNoc/+t/rV/gr3h7cZCy3uhWueMfeqg0qEylgHe+E/jhD0unNeq6dUy5\nYoDuwoZ+/+qonDuLoq6v7nMfYz/8fuvx6KPWf1vFAeWlS8ArX7n62eLCil4GbUD5emVERETU9Bgk\nkffcgdTdd1tBwEbu+K9XO6LS9vWO/cAHrIrc7ov/cNgKRq69djXAs4sXrhWc2QFYcTBgj1jZ0xTt\nulnFgZY7+PrCF8r/HTIZaz1YuToSxRXvKxUjdBcLrPQ9tVS4X14G/tt/q+7YrZJKWVMm3/KWwu3F\nWReL16y535fL1FicDr9ccGe/f/RRBkhEREQtiEESbb211kqtxYusdgDwG79hTXerJnArV9jO/d5d\n0d1O024Yq2uh7H0jI6XBljvQAqyK6pWmJv7t31qv3d9R7m9TblpdtdvcbbN/71tvLV84eccOK6gr\nriq/VhX6cs/rVavP50ur1hsG8P73l7bJ9ra3labUd6fWL7ev+FHu2GzWCnrX+g4GSERERC2JQRIR\nUH3gZk8Rs6eRbVRxYFUu2PqDP7CmsRVPTXzHO6xn+3uAwtEx9+iPYazur3Rcpc+W81/+C/DHf1yY\nwj0UAt785tWRmErcQa59nP1cHAAXT30rd5x726c/XbnK+9vfvvp7up8rqXZ/Nce99KXlg0oiIiJq\nagySiBphvYACAH7zN4GensYlIygXDExOAvv2Ae99L/Dcc1Zl9w9+EHjta639a41SeWmtKu/793v/\n8yv56Ee5JomIiKgFMUgiamYbnZpYD5Wm4L3hDdajmdh/o2bLbudu1+nTjW0LERERVY1BEhFtD40M\nKNey0q7HRR5vdFOIiIioOuvM9yEiIiIiImovDJKIiIiIiIhcGCQRERERERG5MEgiIiIiIiJyYZBE\nRERERETkwiCJiIiIiIjIhUESERERERGRC4MkIiIiIiIiFwZJRERERERELgySiIiIiIiIXBgkERER\nERERuTBIIiIiIiIicmGQRERERERE5MIgiYiIiIiIyIVBEhERERERkYvnQZKI3C4iT4vICRF5d5n9\nu0XkayLyhIhMicjPet0mIqJ2wn6YiIioNp4GSSKiA3gQwMsAXAPgNSJyTdFh7wXweaXU9QBeDeBP\nvWwTEVE7YT9MRERUO69Hkm4EcEIp9axSKgvgcwDuLDpGAeheed0D4LzHbSIiaifsh4mIiGrkdZA0\nDuA51/uzK9vcPgDg9SJyFsAjAN5W7otE5B4ROSYix2ZnZ71oKxHRdlS3fhhgX0xERO2hGRI3vAbA\nXymldgL4WQD/Q0RK2qWU+oxSalIpNTk0NLTljSQiqpVSCqYyYSqz0U1ZT1X9MMC+mIiI2oPP4+8/\nB2CX6/3OlW1ubwRwOwAopf5DREIABgHEPG4bEbUYO+hQWHl2vS9+bT8DKHjtDlrs1+7P299r/wz7\n86a5ctzKaxPWeyiU/Q4AgMCayAYAOgJb+bdyYT9MRERUI6+DpMcAHBSRfbBOyq8G8NqiY84AeCmA\nvxKRqwGEAHAOB1GLcAcklQKY4sBDKYW8mYepTOfZMA0YyrBeKwOmaTrv7WMggEBWAw+xfr5AoKAg\nsrLPFZwosQIZBes4EbG+A3Bei4jz+xTvK3ucADp066W29vcBQCqXWv3ircd+mIi2hFIKWSOLjJGB\nYRrQRIMmGnRNd15rokEXvaSfJGo2ngZJSqm8iLwVwFEAOoDPKqW+LyL3ATimlHoYwDsA/LmI/A6s\ny5pfVfYtXyJqGDs4sR85I4d0Po10Po2MkUEmn1kNXlY4QQoAJySwA5SVfWrlAE00iAi0lVldAinc\nJoBf8xdsp9qxHyYiL+SMnHMuiGfjWM4sI5lPArCCJffNKvc9IgVrnwYNPs0HXdPh1/zwaT7n4df9\n8Gv+wsCKgRZtMa9HkqCUegTWQmD3tve5Xh8H8JNet4Oaj31xXe7OO3nLMI2CAChrZFcDoHwGGcMK\ngOwTmx3Y6JrunMRCvhCDlxbBfpiINsowDWSMDLJGFolsAvFsHPFsHHkz7xzj1/0I6AH0BHuqPifY\nsw0MZTjnIXv2gWEazuyBegdaRNXyPEgisimlkMqnkMgmMJecw+Xs5YKpUQV3hzSBDh2apkGDBk3T\nnBEFXdOhS+EdJfthj0K4p1W5n9fb1+qUUs4Jxx79yRk5pPIpZIyMEwQpqJLP2Scan+ZDZ6BzW/w9\niIioOu6pcqlcCsvZZSSyCaTzaYgIlFLQNR0BPVCXc4SIQBfdmbq80TZXE2gJxJnyDWmKpGXUAhgk\nkaeyRhbJXBILyQUsphedO09hfxi9wd6CO06V1rYYyoAyVelietfx9vaVkGf17pPr2V67UmkfsNpp\nFwRdK0FapcDMHdzpml42AHNPGasmgCvHXsfjBEBmbnUEKLcyBc7IAMWTpARWACRWANQd7OboDxFR\nG1tvqpyIIKAHENADCPvDDW5tZbUGWoupRY9bRNsJgySqK8M0kMqncDl9GXOpOaRyKYgIfJoPHf6O\nNYe6nYXyrkXxW6048LKfDdNAHvmS7fZn3NnNrF8GECUlAVhxcOZeo2N/zj2qZj9MZSJrZkumG2jQ\nnGkGft2PkC/keQD0xae/iI9/++O4sHwBo5FR3Hvzvbjjyjs8/ZlERFQ791S5ZDaJ5eyyM1XOHh3a\nyBpCQBIAACAASURBVFQ5onbAIIk2RSmFjJFBPBPHfGoelzKXoJSCJhrC/jD6wn2NbmJN3IFaI5UL\nxjq1zsY2ClaA9N6vvRfpfBoAcH75PN77tfcCAAMlohbinpoLAEE9yAvkFuaeKpfOpZ1gqNxUufVu\nWBKRhUES1Sxn5JDMJbGYXsRiahFZIwsIENJDvBNVJ80SrNkM08C55XP48Dc/7ARItnQ+jfd//f04\nu3wWXf4uRIIRdAW6EAmsPLve+3W/Z23kCBdR6bpEwzScqVXuzJRZM2uNdsMaldY1Hd3BbvSF+tDh\n70DIF+KFdBNRSiFn5gqmXKdzaaTyKaRyKSTzyYJZCwE9AL/mb7kblUTNhEESrctUprOIcz45j3g2\nDoHAp1sZzjoDjR/hoPpIZBM4tXQKzy4+6zxOLZ7CqaVTyJm5yp/LJfDJb39y3e8P6kEnaLIDp0gg\ngq5gFe9XArCwL1wSiHOEi9qBHfQUXCivZKXMGllnJKFkGi9QkJgl6AuWjEzb/fxSasn5bCQYQW+w\nF13BLoR9YU9vcrSzcutNM/lMYcZRM+MkH7DrwrmzjfIGJVH9MUiisjL5DBK5BOaT81hKL1lJEUTQ\n4e/gnakWp5RCLBHDs0vP4tmFlWBoyXq+GL/oHKeLjl09uzDRN4EX7n0hJnon8PH/+DjmUnMl3zkW\nGcPR1x91UsMuZ6ypHvaUj+XMMuK58tvnknPO+3g2vm77ddERCUTQGehEJGgFUVMzU9bFoUs6n8aH\nvvEhDHUOoSfYg+5gN3pCPej0d/JigppKceBjKMO5SLZT8meMjLOg3mZnpdREc1Ieb3Rdoj1F2l6k\nb0/fOrd8DmrZmvob8ofQF+xDd6gbYV8YQV+wbn+D7ay43py75II9uueuJ2evN7UDoIAvgA6to9G/\nBlHbYZBEAKxOPJlLYim1hIXUgnPBGfRZd/6ZDrr1ZI0szlw6UzAqZD8SuYRzXKe/ExN9E7hp/CZM\n9E1gf99+TPRNYFfPLgT0QMF3BvRAwYgNAIR8Idx7870I6AH0h/vRH+7fcJtNZSKRTawGUdllxDPx\ndd8XB0i2xfQifuWffqVgmy46IsHIauAU7EF3qHv1teu5O1S4rSvQVfMF6Bef/iI+9h8fA4ZwaMN/\nGGp56XwayVyyMPgxM8jmswWjPvaUKbvUwWaDn40SEQR9wYJAKGfkMJeaw4X4BQDW6FRvqBe9oV4r\nwCozyrvduQPcnJkrKLqdyqeQyWcKagICKxnZtMb92xJRdRgktSm7ZtFyZhkLqQUsZ5cBWBeQIV8I\nHYH2umvVrOtZqmnXUnqpJAg6tXgKz11+DoYynONGu0Yx0TeBX7j6F7Cvbx8m+iYw0TuB4c7hqk/Q\n9s/26m+liWaNDgUjNX3ulr++BeeXz5dsH+oYwsd+5mO4nLmMS5lLzvOl9KWCbWcvn3Veu/9m5drX\nHeguCarcwZY7qPrPi/+JTx37VMUgjrYvwzSQzCVxKXMJc4k5678BgVNKwKf54BMfQsHWuUD26/6C\nKXeGaeByxspkCmVd/PcEe9Ab6kVnoBNhX7il1zW513XZr1P5lFN2IZ1Pw1CG8+9nj/Sx5ALR9iBO\nyuIWMjk5qY4dO9boZrQcu1r2YmoRC+kF6+6WsmoWBfRA23bkxetZAGvtzNtufBteMvGSgvpI5Wol\n6Vrhfnetpc38Tcu1K6AHcPv+2xH2h/Hs4rM4uXgSC6kFZ79f82Nf777VIGjlsa9337ZeO1bubxXy\nhfDBWz5YUwCnlEIil8DlzGUriCoKpuzny+ky2zKXCyrQl/g0oM6rbfU/GfviQnbNmYWUVRfOzijW\nLut57Gyn6XzaGRHr9HeiL9yHSDCCkC9UMjrdCMXTG/NmfnVaY9H0RgAFQZB7bZc91ZEKNetNR8Cq\nk3Tz7pufUKa6odFtoebHIGkbsTt+d2ajrJFFJp/BUmYJ6Zx1AWkXh2u3zj2ZS+LMpTM4c+kMTl86\njTNLZ3Dm8hk8du6xNUcPNkNgTauwnysFU3aw5RwHDWeXz1a86O4N9TrT4tyP8ch4S9+53YxGn5iV\nUkjmkgWB0xv+8Q2rBzBI2nZMZVr/5unLmE3NOn1s0Bdsy6ln5djrbwzT6mODehC94V70BHsQ9ofr\nmnrcMI2C858d/NiJLdJGuqrpjfZr/vvVrl43rLzCIIlqwel2LWCtRb12NiN73nNJsdGVi++AHmiL\nhAuXM5dxeum0FQxdPoMzSysB0aUzmE3OFhzbH+7Hnp49awZIH/uZj8FUJkxlwjANmDBhmqbzbCjD\n2e8cZ2/b5HE/uvSjsm0SCL7zpu/U88+2Ldxx5R0NPQmLCDoDnegMdGI0MgrASmhRbhogtS57RH4+\nNY+F1EJL14XbCgE9UDB6lDfzWEgtIBaPORnaeoI9VurxgJV6vPgGnqnMwnOgaTjBl5PavMI50L45\n1YrTG1tJPBvH+eXzFctE3P+t+3H96PXoD/ejw99e0/mpdTFIaqBy6VyLh/yzxupdL/eon7vj10Vv\nm3nPSikspBZWR4NWnp+79BxOXzqNpfRSwfHDncPY07MHL9zzQuzp2YPdvbuxu3s3dvfsdta9VFrP\nMhYZw89f8fNb8nuV88TFJ8q2y74A32p2rZWckXNS0NoXJHZA7tf98Gv+tphaVI17b7635K4qtRY7\nNfblzGXMJmeRzCadpAbdwe62G5HfLJ/mQ1egy3lvKhOJXMKZNiwiiAQi8Gk+5zzoHlF3Z3+zp73Z\nyVj4b+GNnJHDTGIGF+MXcX75PC4sX8D5uPV8IX4BF5YvOOuaK5lLzuGlf/NSANbI0kB4AH3hPivZ\nT8hK+GO/HwgPOEmA+sJ9dclI6p5pgGFcu6kvo7bBIKnOyhXyq1TLolw610ZnNNoK602LMpWJ2cQs\nTl86XRAAnbl0BqeXThdkZtNEw2jXKPb07sFt+2/Dnp492NO7B7t7dmNX9y4nne1ayl3I2hnbGqmR\n7bID9qyRdaq1h3wh9IX60BPqQVAPIuQLwVQmcqaV0jaVSyGejSOZTyKejjs1PQAUBE/tdCFj/3f9\nsf/4GC7gQoNbQ9XKGTmnBMJCylq/aY8W9XdsPHsjldJEQ4e/wxldsFOPZ40sdE1HZ6CzrfqMraaU\nwlJ6CRfiFwoCoIvxi9br5fOIJWIldbd6g70YjYxiZ/dO3Dh2I0YjoxjtGsWHv/HhsmUi+sP9eMeP\nv8Nar5dadEZi55PzOLFwAguphYo3k9yZU/tD/ejvWA2s3MGV/YgEIgXXTSVTADU0fmEctQSuSdoE\n93zn5ewyljPLSOVTBYX87AtMdy2Ldp7vXG6+sl/z4yd2/gR8ug/PXXoOZy6fKdjv03zYGdmJ3b27\nrdGgnt3OY2f3zrosBG70epZGtsswDWf0ErCm8wX0ACLBCLqD3Qj7wwj5QvBp1d9TMZXp1AOxa24l\nc0kkc8mCu8Lu0Sef5tu2/0+kcin82K4fm1Y5dbjRbamnZumLN8ud7XM2MYtELgERgV/zt+X6TWoN\n1ZwfMvmMM9pzfvm89brofXFw4tf8GIuMOYHPaGQUY10r71e2VZoyt5k1SclcEvOpeSu5VGphzcdi\nehHJXLLs9/g1f0Hg9MSFJ6xrM9s2XB9K3mCQVAX7zlbGyDjTLuLZOHJGDhBrv1/3I6AH4Nf82/ZC\nbzOyRhbTsWnc88V7Kg7LH+w/WBAA2dPjRrtGa7pAp8pMZTpTWNzpaiOBCHpDvQj5Qgj5Qp5Ol7MT\nithrCuLZOJK5pHODweYOnlo9GQWDpOZjjxYtJBewkF6AYRrOaFEzZGCj5tFsN9FMZeILT30Bf/Sv\nf4S0UXhD8ebxmxH2h3F+2RoNmk/Nl3x+qGMIO7p2WIGQHQRFxpxt/eH+Td0Y2Kq/VzqfdkajFtIL\nFYOrJ2eeLPwggySqEq88i7jvqtupgOPZeEEq0IAeQNAX3NYplTcrkU3gyZkn8dj5x3Ds/DFMzUyt\nuS5DIPjSa7+0hS3c/kxlOiM59oJmXbPm7u/o2oEOfweCvuCWXxDaqXOL70S6p9lkjSySuSQS2QQS\nuQTyZt65+SAQ+HUreOJNCaqWe7RoLjmHeDYOwArGO/2dLR+IkzeKR0bOL5/He7/2XgAoe+FvmIZT\nSymVTyGds55TOdc2175kPukcU7A/lyp4be9L5VIV667lzTy+9dy3MNE3gdHIKK4durZwNGglEPK6\nz9+qJDohXwhjkTGMRcbWPK7SumOi9bR1kGQvRE/lrBPncm4Z6VzauuhScLLC9QR7eCG2jsXUIh6/\n8DgeP/84Hjv/GI7PHoehrDuzVw9ejVdd+ypMjk3ig//2QcwkZko+36hkBNuFe7TTMK3ihgJrAfRQ\n9xA6A50I6sGmrodlL4YP+oIAgAEMOPvs0Se7mn08G0cil8BSZgmiVpNH2Gv5/JqfF72EvJm3asOl\nFzGfnLeCbQgz0VFV4tk47v/W/WWztb3nq+/BXzzxFwUBTjKXRM7M1fxz/JrfGcW3pzd3+DoQ8ofQ\nE+yxtvvCCPlDzusHH3uw4vc98rpHam7DdsYEOrRRbREk2QXuMvkMkrmks37IvTbCTlPKE2d1Lixf\nwLHzx3DswjEcO38MJxZOALD+jkdGjuDuG+7G5Ngkrh+9viCTUSafacokCa1EKYWcmUMmv5r1yc4I\n1R/uR1egyyna2KwBUa3s0Sf4gR70YAQjAFaDQzt5RCKbQCKbQDJvrX1yjwDbiSM4+rQ92DVxnPT8\nK2n1DdNAzsxhMbWI5eyyMx26w9/BwJnKWkwt4uTiSZxcOIkTiydwcuEkTi6exMX4xYqfyZk5jEfG\nneAl7AuvBjor2+xAx97nPPvDCPvCTp2ojUxv/scf/GNTZT9tZvaolj0FUJkq2+AmUYvYdkGSO412\nPBPHcnYZ8VzcWeugiYaAHuAJswZKKZxaOmUFRSuPc8vnAFjV1G8YvQF3XHEHJscmcXj4sDMSUE5x\nZ9UM87s3yr2er1ya9nIFC4u3r7ff3m4ow1oDBwACdPm7MNI14gRE9SzI2Eqc0SdY/831h1czj9m1\nVNzJIxLZBC5lLjnHKCjoojsBFNe+bS07G6g7yLGDHndB7JyZQ97II2tao4numwN2ohz3a000BH1B\nzgIgh1IK8ykrk5odDNmv3et2wr4wJvomcNP4TTjQfwB/+Z9/6aQndxuLjOFPf+5Pt/JXKNCsWVmb\nlT0FcDG1iJv/8ObvN7o91Bpa+orAvvhJ59O4nLmM5cwysmbWOlGKgl+zkin0Bnt5oqyBYRr4wdwP\nrFGic8fw+IXHnZPIQHgAk2OT+JUf+xU8f+z5uHLgypqDzUYX/SxmzyG3gxA7I6HD/k+nKLZxL2wt\n91ogq//dCZyaQhpcx2qa8zPt/SJSsP4GsEbouoPdVkDkCzLbVhV0TUdYC5ekgbdH4uzse3bWvUQ2\ngWWjMKlIu6Yur5Ud2JQb2bGDGifYMfPO396ECaiV/85XkuAIBEqUM+VZE80qii1WVlC/v/WmUjbb\nwv/tTCmFi/GLOLl4siAgOrlwsuAGSVegCwf6DuDFe1+MA/0HsL9/Pw70HcBoZLTg//XRrtGmDEa2\n0w1HombVskFSdCZakP4x6Asi4AugQ2Ml51pl8hlEY1EcO38Mj51/DE9ceMKpRTQeGcdP7f4pTI5N\n4nljz8O+3n3bIuDMGTkkc0mYyoSu6RjsGHQqgbuDFWA1UHFvo9ZlJ18J6AF0orNgiq2d7MK+AZPM\nJZHIJbCcWXYCZxbOXWUoA989992Cmwp2wAPAyaCoiyvY0XT4dX/bBPu1LvxvZ7UEk6Yyce7yOSsQ\nKpoq566l1xvqxcH+g7j9wO040H/ACoj69mO4c7iq/ryZg5Fmu+FItN20bJCUzCW5fmgdlU448Wwc\n37vwPTx+/nEcu2Blnssa1hTdg/0H8fIrX47JsUlMjk1iR9eOBv8W9WNf9AJAUA9iPDKOnlCPFRgx\n+CFYo4D2ugIUzRrNGbmCwrmJnJV5L56OO6MhCqog816rjXjUbGV6G/viyh741gNlF/7/4b/+IfJm\nHmORMYx3j2NH1462nu5ZKZg0lIEjI0ecdUJ2UPTs4rMFf9ehjiEc6D+AX7j6FzDRN+EERO4puBvF\nYISoPbVvj7zNlTvhvOsr78Invv0JXIhfsEZQRMe1w9fi9Ydfj8nxSdyw44ZtdbGjlEI6n0Ymn4GC\nQlegC3t79zoFUolq4df9TgKA3lCvs92dujxn5pzkEYlcAoYyAGB1PRm1hRMLJ/DoiUdx9ORRxJKx\nsscsZ5fx7q++23mvi46RrhGMR8YxHhnHWPeY9bp7HDsjOzHSNbLt6jcppaxaVakF3P/N8lnk3vWV\ndxVsG4uMYX/fftw0fhP29+/H/j7r0RPq2cqmE1EbYJC0zaTzaRyfPY77/u2+khOOoQzMJmfx5sk3\n4/ljz8d1O66rWDW7VZnKtNKwrlyU9oZ6sbN7JyLByLa7wKDmUJy6vFLyCBhgpLRNKaXwzPwzOHry\nKB498ShOLp6EQPC8seehO9iNy5nLJZ8Z7RrFX9/11zi/fB5nl8/i3OVzOL98HueWz+E7576DmWdm\nYCrTOV4gThBljz7tjOx0Xo9Fxmru47xYK2WYBpbSS5hPzWM+OW89u18XPVeTlvkjt34EE30TmOib\nKMiWSkTkJc+DJBG5HcB/B6AD+Aul1P1ljnklgA/AWhr/pFLqtV63azswlYlnF5/FkxefxFRsCtGZ\nKJ6ef7ogtXmxnJHDb930W1vYSu/lzTySuSQM06rLNBAewEDHADoDnW09fYUaryB5hIK5/ie8wX64\n/pRSeGruKRw9cRSPnnwUP1r6ETTRMDk2idcdfh1+ev9PY7hzuGRUH7AW/r/jx9+BPb17sKd3T9nv\nzxk5XExcxLnL5woCqHOXz+F7F76HR374iDNSaRvuHHZGouzAyf065As5x9ayViqTz2AuOYf51DwW\nUguYS845z8WBz2J6sSC4s/k0H/rD/U7/vK93n/N6IDyAB/79gYpZ5O666q4q/1WIiOrH0ytIEdEB\nPAjgpwGcBfCYiDyslDruOuYggPcA+Eml1KKIDHvZplY2E5/B1MwUpmam8OTMk5iOTTsLVLsCXTgy\ncgRvuv5NODxyGPf9633bumhr1sgimU1CQSGgB7Cjawd6Q73o8He0xWJwomqxH64fpRSmY9M4evIo\njp48ijOXzkAXHTftvAm/9mO/hlsnbsVgx2DBZza68N+v+7Grexd2de8quz9v5jETn8G5ZSuAOnv5\nrBNEPTnzJB49+WjJDbPBjkFnJOobp79Rdnrb+77+Phw9ebQgEHInQnDr8HdgsGMQA+EB7O7Zjet3\nXI/+cL+TCMfeN9AxgO5g95p9s0/zNWUWOdoYU5mFGV6JWlDVQZKIXAHgUwBGlFKHROQIgJcrpT64\nxsduBHBCKfXsynd8DsCdAI67jrkbwINKqUUAUEqVn8DdZuLZOKZj005QNDUz5QQ9fs2PKwevxJ1X\n3YnrRq7D4ZHD2Ne7r+AElMqlttUJx15flDbSgLJOznt69yASjCDsC7Mjpraxgb6Y/fAmKKUwNTOF\nR08+iqMnjuLc8jn4NB9u3nkz7rnhHrx04qXrJgfwYuG/T/NhvNsaJSrHMA3EEjErcFoJnuyA6vjs\ncat+YBnJXBKnl06jv6Mfh4YPOSM99rMdAA2EB+q6trOZs8gBq2sPFZRz8e9+1kTbFuchu3aZqUwo\npWAq03oN5aT3B8pnenVnufRpPuezbgKBrunQNR0+zWel9t/uCW6oZdUykvTnAH4XwKcBQCk1JSJ/\nC2CtIGkcwHOu92cB3FR0zBUAICLfgjUV5ANKqUeLv0hE7gFwDwDs3r27hmY3v5yRwzPzz2AqthoQ\nnVw46RQV3dOzBzeO34gjI0dwZOQIrh68es2CrUDzn3CqYSoTqVwKWSMLEUFPsAfjkXFEgpF1f3+i\nbazWvrhu/fDKMU5fvHPXzg3+Cs3NVCaeuPgEjp44iv9z8v/gQvwC/JofP7HrJ/DWG9+Kl+x7SUHy\njmakazpGI6MYjYxiEpMl+2/5q1twPn6+ZPtYZAxffO0Xt6KJJZopi1zezCOdTyNn5qzslSKIBCLQ\nNd2qCWYaMGDANFaLINsBQbkixwX19opqgilllRWwgy53rT07+CoOzNzHAygJaOxaZXZwYwc9bsWB\njoKCBg1+rTBDp0/zFTx0TS9I61/uYX+3uzC0/Ujn01ZSJcOqc5k1swWlNux2uAMpn+bbFkEotZZa\ngqQOpdR3i/4jrbz4pbY2HATwYgA7AfybiBxWSi25D1JKfQbAZwBgcnJSFX9Jq1BK4ezls3hy5kkn\nIDo+exwZIwMA6Av14bqR6/CyAy/DdSPX4dDwoQ1nnGumE0618mYeqVwKeTMPTTT0hfsw2DGIrkAX\n1xcRWbzoi6vqh4HCvvj6G65v2b64mGEa+N6F7zlT6WKJGAJ6AC/Y/QL89s2/jZfsewm6g92Nbmbd\n3Pvj926r2QabYY8SpfNpKFhBS1APoi/Uh55QD8K+MEK+UFUX6XYwYgcn6712BzflijIbynCCnoqB\nmVhFyu0Axqf5EPAH4BOfU5LA3ucuzrxWcFMv9qhRsLimgotSqiSQsv897BkkyUzS+Xu5uX9nn+bj\ndHuqq1quOudEZD+s+yAQkVcAuLDOZ84BcE+o3rmyze0sgO8opXIATonIM7BO1o/V0LaGq5QlaCG1\ngGgsiuhM1AmMltLWdUdQD+La4Wvx2sOvxZGRIzg8chg7Izvb7m5J1sgimUtCKQWf5sNQxxD6wn3o\nDHSywyMqVWtf3Db9cK3yZh7Hzh/DoycexZef/TLmknMI6kG8cM8LcfuB2/HivS/ettnUtsNsg40y\nTMMawVipD2iPEo1HxtEV7ELYF95wkWhNNGCLT+F20eZWJSJOiYW1FAdSOSOHjJFBKpdCJp9BIptA\n3syXjErZI18+zefM0CGqRi1B0ltg3T28SkTOATgF4HXrfOYxAAdFZB+sk/KrARRnTPonAK8B8Jci\nMghr2sezNbSr4SrVJPrwNz/sZOsRCA72H8RL970UR0aO4LqR63Cg/8CGO+JWZnds9jSGkD+EXd27\nnDt2rdzZE22BWvvituiHq5Uzcvjuue/i6Mmj+PKzX8ZCagFhXxgv2vsi3Lb/Nrxoz4vQGehsdDO3\nRCvONqiVUgo5M4d0Pg3TNJ2Czz3BHvQEe9AR6EDIF2rpG3Ltcs60R4vWYiqzJJjK5DNOzcROfycA\nRkpUnaqCJBHRAEwqpW4VkU4AmlJqeb3PKaXyIvJWAEdhzXP/rFLq+yJyH4BjSqmHV/b9jIgcB2AA\n+F2l1PxGf6FG+Pi3P162JlEyl8Q7f+KdODJ8BNcOX7tt70iuxe6g7HVFAAqmMXT4OwrS0hJRZRvp\ni9ulHwYqj+hnjSy+ffbbePTEo/jqs1/FUmYJHf4O3LL3Fty2/zb81J6f2nY149qVYRrIGBlk8hln\nW6e/Ezu6diASiCDsD7Nm3jamiYaAHlj735jDSVQlKZ7fWfFAkWNKqdLVnw0wOTmpHvynBze8Vqfe\nrvqTq8r+PycQ/OCtP2hAixrDPjll81nn7xHUg+gOdqM72I2wP4ygL8i1RdSWROTxevShzdQXX3/D\n9erPvvhn6As1vi8uV48ooAdwZPgInll4Bpczl9EV6MJL9r0Et+2/DS/Y/QLeoNkGskYWmXzGWruz\nMmW7O9jtlIQI+ULMnkYF6tUX0/ZXy9XqV0TknQD+HoBTNEEpVVr9rc2MRkZxfrk0S9B2qUlUjhMQ\nGVkni09ADyASjKAn0oOw31royoCIqO7YF5dRbkQ/a2Tx+IXHcddVd+G2/bfhJ3f/JEcRWpipTGTy\nGWSMjLMOJ+wLY7hz2CkHEdADbTP9jIi8VcsV7KtWnt/i2qYATNSvOa3p3pvvxX/9yn8tqAewnbIE\nuU9MNl10dAe7Mdo16tyta8f1VUQNwL64jAvLlXNX3H/r/VvYEqoXe/1q3shbaaFFQ0+oBzu6dqAz\n0MkbcUTkqap7F6XUPi8b0sruuPIOfPDfPujk/G/lLEGmMp3pC3bFbF3TEQlGsKNrhzNCxLuxRI3B\nvri8dhzR344y+QySuSQAK6nPQHhgdbq2HuQoERFtmaqDJBHxA3gzgBeubPo6gE+vpIxta/PJeSxl\nlvDun3w3fu36X2t0c6pm14bIGBmnirYmGrqD3RjqGHLu1DEgImoe7IvLu/dm1v1pVaYykcgmkDNz\n6Ap04WD/QUSCEc5OIKKGqmWc+lMA/AD+dOX9G1a2vanejWo10VgUAHB45HCDW1KZHRBljayzwFVE\n0B3oxkB4oCAg4p06oqbGvriMO668A+eWz+ET3/4EAGAsMtY0I/pZI4tELmFNihSgw9eBoK9ycc12\nkTWySGaTgADDncMY7hxmlkEiahq1BEnPV0pd53r/f0XkyXo3qBVNx6ahiYarB69udFMK5Iwckrmk\nNW1OBF2BLuwIr87l5tQFopbEvriC/nA/AODLb/gydvfsbmhbDNNAPBuHqUyE/WFM9E6gK9CFRDaB\ni4mLWEwtAgA6A51tNVqvlEIil0DWyFp/l74J9IZ7ubaIiJpOLb2SISL7lVInAUBEJmDV02h70Zko\n9vftb4oChDkjh0QuAaWsbHNjkTEnFSoDIqJtgX1xBVMzU+gN9mJX966G/Hw7AMgZOfg0H0Yjo+gP\n9xeMjoT9YQx2DiKdT+NS+pITMGmiocPfsW2nmLnPTYMdgxjpGkGnv5PnJSJqWrUESb8L4Gsi8iwA\nAbAHQOsswPGIUgrRWBQv2vOihrXBnrKgoBDUg9gZ2YnecC/CvjBPQETbD/viCqKxKA6NHNryfi+d\nTyOVS0FEMBAewFDnELoCXdBEq/iZkC+EUFcII10jSOaSWEotYSYxg3g2Dl3T0envbPn6PkopJHNJ\nZI0sAnoAe3r2oC/c11YjZ0TUumrJbvdVETkI4MqVTU8rpTJrfaYdXIhfwHxqHodGDm3pz80aYSWO\n4QAAIABJREFUWSSyVomUkD+E3b270RO06hMR0fbFvri8VC6FH87/ELfsvWVLfl7OyCGRTcCEie5A\nNw70H0BPqGdD08Y6/B3o8HdgNDKKZC6JhdQCZhIzMEwDft2PDn/HmgFXs8mbecQz1lTD/nA/9vfv\nRyQQ4U07ImoptWS3ewuAh5RSUyvv+0TkjUqpP13no9tadGYlacOw90kbMvkMUvkUlFLo8Hdgb+9e\n9IR6WDWeqI2wLy7v+NxxGMrAkZEjnv0Mdxa2oB7Erp5d6Av31a0PFhF0BjrRGejEePc4EtkE5lPz\nmE3MwjANBH3Bpp46ncqlkM6noWs6dnbvxEDHABNUEFHLquWW191KqQftN0qpRRG5G6sZltrSdGwa\nfs2Pqwav8uT70/k0UvkUoICuQBf29e5Dd7CbJx6i9sW+uAwvb1glc0mk82loomG4cxiDHYOer6fR\nREMkGEEkGMGu7l2IZ+OYTc5iPjkPAAj6gk0xpdqdoKI72I09vXvQHexuqZEvIqJyagmSdBERpZQC\nABHRAbT9xOJoLIorBq6o2xxrpRTS+TQy+QwUFLqD3RjrHUMkGGFgREQA++KyojNRjHaNYqhzqC7f\n507b3Rvqxd7evYgEIg1ZJ6RrOnpCPegJ9WBv714sZ5YRS8SwlF6CQBDyh7Z8RoFd9FVEMBYZw0B4\ngNO9iWhbqSVIehTA34vIp1fe/8bKtrZlKhPTsWn83BU/t6nvUUohlU8hk7eWFfSGerGzeyciwQgX\nuBJRMfbFZUzFpjY9imSPihjKcNJ294R6mqof9mk+9IX70BfuQ87I4XLmMmbiM1hILkDTrAx5XrXX\nPd2w09+JA/0H0BvqbfkEE0RE5dQSJL0LwD2wKr0DwJcB/EXdW9RCTi+dxnJ2GYeGa0/a4M76IyLo\nDfZiT88edAW6tm0KWCKqC/bFRZbSSzhz6Qx+6Zpfqvmz7ro9ldJ2Nyu/7sdAxwAGOgaQyWdwOXMZ\nF+NWSnGBtb6pHueT4qKvQx1DTVHygojIS7VktzMB/BmAPxORfgA7lVJtXZtjOjYNoPo58KYykcwl\nkTNyAID+jn4MdVipYllIj4iqwb64lLMeaaT6kST3dLH+cD+GO4fXTdvdzIK+IIZ8QxjqHEIql8JS\negkzcSuluCYaOgOdNZ1nWPSViNpdLdntvg7g5SufeRxATET+XSn1Ox61relFY1GEfCEc6D9Q8Rh3\nYGSfjO3AiFMUiKhW7ItLRWNRCASHhtYe1c8ZOSRzSZjKRFegCwf6D6A72L3tRu/D/jDC/jB2dO1A\nKp/CQmoBsXgMOdMqctvh76h4/skZOcSzcQBg0Vciamu13BLqUUpdFpE3AfgbpdT7RWTKq4a1gmgs\nimuGrim5s2aYBlL5FLJGFrroGAgPYLBzcFsUBySihmNfXGRqZgr7+vYhEoyU7HPfqAroAezs3lnX\ntN3NTEScGkzjkXEkcgksJBcQS8aQN/MI6AGnBpOdwS+oB7G3dy+LvhJR26slSPKJyCiAVwL4fY/a\n0zLyZh7HZ4/jlde+smDbcmYZuqZjsGMQ/eH+lp6+QURNiX2xi1IK0VgUL9j1goLtds0eEcFQhzUN\nrZ1HREQEXYEudAW6sLNnJ+LZOOYSc5hLzUEphb5QHyb6Jlj0lYhoRS1B0n0AjgL4plLqMRGZAPBD\nb5rV/E4unEQ6ny5Yj5TKpTAaGcXO7p0MjIjIK+yLXS7GL2IuOVewHmkxtYieUA929+xGd7CbI/hF\nNNHQHey26hqZe2Aog6NGRERFqr6SV0r9g1LqiFLqN1feP6uU+kV7v4i8x4sGNqtorLRwYd7Mo9Pf\nyQCJiDzDvriQ3RcfGTnibBMIJvom0BfuY4C0Dl3TGSAREZVRz6v52nOvtrBoLIpIIII9vXucbSLC\nzD9E1Ght1RdPzUzBr/lx1eBVzjYFxb6YiIg2pZ5BUltNYo7ORHHt8LUlo0Y8MRNRg7VXXxyL4oqB\nK5zREFOZ0EXniD4REW1KPc8iqo7f1dSyRhbPzD9Ttj4SgyQiarC26YtNZWI6Nl0w1S5v5tsicx0R\nEXmLI0kb8IO5HyBn5hgkEVEzapu++NTSKcSz8YK+2DANBH3BBraKiIi2g3oGSf9Qx+9qak7SBlc2\nJaUUBMJFwkTUaO3TF8+UJm0wlMGRJCIi2rSqgiQRuU1E3igie4u2/7r9Win14QqfvV1EnhaREyLy\n7jV+xi+KiBKRyeqa3jjTM9PoD/djtGvU2ZY387x7SUSe2mhfvB37YcAKkjr8HZjom3C2cbodERHV\nw7pBkoh8GFbBwsMAvioib3Ptfus6n9UBPAjgZQCuAfAaEbmmzHERAG8H8J3qm94407FpHB4+XFBw\nj3cvichLG+2Lt2s/DABTsSkcGjpUOIKvAL/ub1yjiIhoW6hmJOkOAC9RSv02gOcBeJmIfGJl33pz\n328EcGKljkcWwOcA3FnmuD8C8BEA6eqa3TjJXBInFk+UrEfKm3kEdY4kEZFnNtoXb7t+GLAS6Dw1\n+xQOjRwq2ce1oUREtFnVBEk+pVQeAJRSS7BO1N0i8g8A1qtANw7gOdf7syvbHCJyA4BdSql/qbrV\nDXR89jhMZRasRwKsxcIcSSIiD220L952/TAAPDP/DHJmrmA9ko1BEhERbVY1QdJJEblFRHYBgFLK\nUEq9EcDTAK7ezA8XEQ3AxwG8o4pj7xGRYyJybHZ2djM/dlPshcKHhgvvXpowWbWciLzkSV9cSz+8\ncrzTF8/NzW30x27a1MwUADDLKBEReaKaIOmXYM1Rf8S9USn1XgC71vnsuaJjdq5ss0UAHALwdRH5\nEYCbATxcbtGwUuozSqlJpdTk0NBQFc32xnRsGqNdoxjsGCzYLhCemInISxvti+vWD6/8PKcvHhwc\nLHfIlojGougP92M8sjooppQCBNCFWUaJiGhz1g2SlFIppVQSwPdE5PlF+85V+JjtMQAHRWSfiAQA\nvBrAw67PX1JKDSql9iql9gL4NoCXK6WO1fqLbJVoLFr2ziXAu5dE5J1N9MXbrh8GrFH9cgl0Alqg\nYBsREdFG1FIn6SYA/yEiJ0VkSkSiIjK11gdW5s+/FcBRAE8B+LxS6vsicp+IvHzjzW6MS+lLOH3p\ndMl6JBuDJCLaAjX1xdutHwaAeDaOEwsnStYjsZAsERHVSy1X9bdt5AcopR5B6fSQ91U49sUb+Rlb\nZTo2DaB0PRIAKCgGSUS0FWrui7dTPwxYCXQUVNkso53+zga1ioiItpOqr+qVUqe9bEgriMbKJ20w\nTE7xIKKtwb54NYFOSZZR1qsjIqI6qWW6Xdubjk1jb89edAe7C7YbilM8iIi2ylRsCuORcfSH+wu2\nG6aBkJ9BEhERbR6DpBpEY9GyhQtZSJaIaOtEZ6Jl6yMxyygREdULg6QqzSZmcTF+sWxmOxaSJSLa\nGgupBZxbPscso0RE5CkGSVWqtB4JsEaSGCQREXnPXo9UbiQJYJBERET1wSCpStOxaWii4Zqha0r2\niXCKBxHRVpiamarYFzPLKBER1QuDpCpFY1Ec6D+ADn9H2f08MRMReS8ai+JA3wF0BgpTfZvKhC46\nNOFpjYiINo9nkyoopRCdiZadamdjkERE5C2lFKZmpiom0OG0ZyIiqhcGSVU4t3wOi+nFiguFAQZJ\nRERes/vicuuRDJOlGIiIqH4YJFVhOjYNAGWDJKUUBAJd07e6WUREbWVqZgpA+b6YhWSJiKieGCRV\nIRqLwq/5ceXglSX78maedy+JiLZANBZFQA/gioErSvZxuh0REdUTg6QqRGeiuHLwSgT0QMk+3r0k\nItoa0Zkorh68umxfDAX4df/WN4qIiLYlBknrMJWJ789+v+J6pLyZR1DnSBIRkZcM08D3Z79fsT4S\nwLWhRERUPwyS1vGjpR8hno1XDJIMkyNJREReO7l4EslckllGiYhoSzBIWkc0ZlV3PzxSPkgyYZaf\n+kFERHUTnbH6Yo4kERHRVmCQtI7oTBRhXxgTfRNl9wuEJ2YiIo9NxabQFejC3t69JfuUUoAAujDL\nKBER1QeDpHVMx6ZxzdA1awZCDJKIiLwVnYni8PBhaFJ62jKUgYAWgIg0oGVERLQdMUhaQ97M4/js\n8TWLyAIMkoiIvJTJZ/D0/NNrrg1lKQYiIqonBklrOLFwAhkjU3E9EgAoKAZJREQe+sHcD5A38xXX\nIzHLKBER1RuDpDXYC4XXunvJKR5ERN6ampkCUDmBDuvVERFRvTFIWkM0FkV3sBu7e3aX3W8oTvEg\nIvJaNBbFUMcQRjpHyu43TAMhP4MkIiKqHwZJa5iOTePQ8KGKI0Wc4kFE5L2pmSkcHjlcsS9mllEi\nIqo3BkkVrLdQGGAhWSIiry1nlnFq6dSa9ZEAJtAhIqL6YpBUgb1QeK0giSNJRETemo5NA6i8NtTG\nIImIiOqJQVIF0ZiVtOHQ8KGKx4gI/Lp/q5pERNR2qumLmWWUiIjqjUFSBdGZKAY7BrGja8eax/HE\nTETknamZKezp2YPeUG/Z/aYyoYtetsgsERHRRnl+VhGR20XkaRE5ISLvLrP/XhE5LiJTIvJVEdnj\ndZuqMT07jcPDlRcK2xgkEVGza9V+GLBGktaqVZc381wbSkREdedpkCQiOoAHAbwMwDUAXiMi1xQd\n9gSASaXUEQD/E8ADXrapGvFsHCcXTq45vcPGIImImlmr9sMAEEvEcDF+cd0EOizFQERE9eb1SNKN\nAE4opZ5VSmUBfA7Ane4DlFJfU0olV95+G8BOj9u0ruOzx6Gg1jwxK6UgEOiavoUtIyKqWUv2w8Dq\neqS1MtuxkCwREXnB6yBpHMBzrvdnV7ZV8kYA/7vcDhG5R0SOicix2dnZOjaxVHTGOjGvN8Uj4At4\n2g4iojqoWz8MFPbFc3NzdWpieVMzU9BFx9WDV1c8htPtiIjIC02z0lVEXg9gEsBHy+1XSn1GKTWp\nlJocGhrytC3Ts9MYj4yjP9xf8RhDGQjpPDET0faxXj8MFPbFg4ODnrZnemYaBwcOIuwPVz5Icdoz\nERHVn9dB0jkAu1zvd65sKyAitwL4fQAvV0plPG7TuqIz0XXXI/HuJRG1iJbsh5VSiMaiODK8dhFZ\ngEESERHVn9dB0mMADorIPhEJAHg1gIfdB4jI9QA+DevEHPO4PetaTC3iucvPrRskGSbnwRNRS2i5\nfhgAzlw6g0uZS2tOe7axXh0REdWbp0GSUioP4K0AjgJ4CsDnlVLfF5H7ROTlK4d9FEAXgH8Qkf8U\nkYcrfN2WcKq7r3NiNmHyxExETa8V+2GguqQNNo4kERFRvXl+ZlFKPQLgkaJt73O9vtXrNtRietYK\nkg4NrT2SJBCemImoJbRaPwxYSRtCvhAO9B9Y+0ABdGGWUSIiqq+mSdzQLKIzUezr3YdIMLLusQyS\niIi8EY1Fcc3QNWv2s3kzj4AWWLfoNxERUa0YJBWJxtZP2mBjkEREVH95M4/js8fXrFUHsJAsERF5\nh0GSy0x8BrFErKqFwgoKfo1rkoiI6u3Ewgmk8+l11yPlzTyCOoMkIiKqPwZJLvZ6pGruXvo1P6d4\nEBF5YGpmCkAVfbFillEiIvIGgySX6Ex03erugHVi5t1LIiJvRGei6An2YHfP7jWPM0wDIT+DJCIi\nqj8GSS7RWBQH+g+sXd0dK1M8OA+eiMgTU7EpHB4+vO5oPbOMEhGRVxgkrVBKYXpmuqr1SCwkS0Tk\njVQuhR/O/7CqvhhgAh0iIvIGg6QVZ5fPYimztO4ceICLhYmIvHJ87jgMZTBIIiKihmKQtCI6Y1V3\nryZIAgC/zsx2RET1VktfrKAYJBERkScYJK2IxqLwa34cHDhY1fE8MRMR1V90JoodXTsw3Dm85nGm\nMqGLDk14GiMiovrj2WXF9Mw0rh66GgE9UNXxDJKIiOovGoviyPDa9ZEAa9oz14YSEZFXGCTBuiM5\nPTtd9VQ7gEESEVG9LaWXcPrS6aoT6DDLKBEReYVBEoBTi6eQzCWrmwOvFDTRGCQREdXZdGyloHc1\nQRLr1RERkYcYJMGa3gEAh4YPrXts3swj4KtuSh4REVVvamYKAHBoqLq+mNPtiIjIKwySYC0U7vB3\nYKJvYt1jDWUgpPPETERUb9FYFBN9E4gEI+sfrJhllIiIvMMgCdaJ+dqha6Fr+rrH5s0858ETEdWZ\nUgrRmSjXhhIRUVNo+yApZ+Tw1NxTVZ+YDdPgFA8iojqbScxgNjmLIyPrZ7azcSSJiIi80vZB0g8X\nfoiska1qPRJgZcKrNk04ERFVx16PxJEkIiJqBm0fJDnV3avIpmTjiZmIqL6iM1ZB76sGr6ruAwLo\nsv4UaSIioo1gkBSLojfYi13du6r+DIMkIqL6mopN4YqBK6pa85k38whoAYjIFrSMiIjaUdsHSdOx\naRwaPlTTyZZBEhFR/ZjKxHRsuur1SCwkS0REXmvrICmdT+OZ+WeqXo9kY5BERFQ/p5ZOIZ6NV70e\nKW/mWUiWiIg81dZB0lOzT8FQRtXrkQzTgE/zQZO2/rMREdWVvTa06pEkxSyjRETkrba+2o/GVpI2\nVJv+Wxm8e0lEVGe1FPQGVkox+BkkERGRd9o6SJqOTWOoYwgjXSNVHZ838zwxExHVWTQWxaGhQ1UV\n9AYAgXDaMxERecrzIElEbheRp0XkhIi8u8z+oIj8/cr+74jIXq/bZIvGojWtR2IhWSJqRc3cD2eN\nLI7PHsehEa4NJSKi5uFpkCQiOoAHAbwMwDUAXiMi1xQd9kYAi0qpAwA+AeAjXrbJFs/GcWrxVE31\nkbhYmIhaTTP3wwDwzPwzyJm5qtcj2RgkERGRl7weSboRwAml1LNKqSyAzwG4s+iYOwH89crr/wng\npbIFxS+mY9NQUDVVdwcAv+73qEVERJ5o2n4YAKZmpgBUvzYUABQUgyQiIvKU10HSOIDnXO/Prmwr\ne4xSKg/gEoCB4i8SkXtE5JiIHJudnd10w6Zj0wDA9N9EtN3VrR8GCvviubm5TTcuGouiP9yP8Uhx\nk8ozlQlddGYZJSIiT7XMWUYp9Rml1KRSanJoaGjT3xeNRTEeGUd/uL+mzzFIIqJ25u6LBwcHN/19\n0ZkoDg8frrqgd97Ms5AsERF5zusg6RyAXa73O1e2lT1GRHwAegDMe9wu68Rcw3okG4MkImoxTdsP\nx7NxnFg4UdN6JCbQISKireB1kPQYgIMisk9EAgBeDeDhomMeBvArK69fAeD/KqWUl41aSC3g3PK5\n2ubAKwURpp0lopbTlP0wAByfPV7z2lDWqyMioq3g6RW/UiovIm8FcBSADuCzSqnvi8h9AI4ppR4G\n8P8D+B8icgLAAqwTuKfs9Ui1nJiZ2Y6IWlGz9sOANaIPoOYsoxxJIiIir3k+LKKUegTAI0Xb3ud6\nnQbwS163wy0ai0IguHb42qo/w7uXRNSqmrEfBoCp2FTta0MVs4wSEZH3WiZxQz1FZ6LY17cPXYGu\nqj/DxcJERPUVnYnWXB8J4NpQIiLyXtsFSUopTMema66PxMXCRET1s5G1oTaOJBERkdfaLkiKJWKY\nTc7WfGI2lcmRJCKiOtnIeiQbR5KIiMhrsgUJjOpORJYRwHMwYWzZD9WgI4c0FMw6fNsggM1XYWys\nVv8d2P7Ga/Xfodb271FKbb7IWxMRkTgCOLPlfXEWyTp9W7v9N9iMWv13YPsbr+37YvJGq96Oe1pl\n1GSjG7FRInJMqdZtP9D6vwPb33it/ju0evvr5Afsixun1dsPtP7vwPY33nb4Hag5td10OyIiIvp/\n7d15rFxlHcbx70PtQtgXQaAkbYHKErGUFjEWBCwgldhiQEpQiCCrIEIgqSFiJW6VYIgJEVkFZSki\npixhp2wGW7ZulLRclmCbStGGzWCl9Ocf551yOs7MnXvbe8+8l+eTTO6Zs8w857z3/s68Z947Y2Zm\nrbiTZGZmZmZmVpJrJ+nqqgNsoNzzQ/774PzVy30fcs+/MeR+DJy/ernvg/NXbyDsg3WgLD+4wczM\nzMzMrK/k+k6SmZmZmZlZn3AnyczMzMzMrCSrTpKkr0paIqlL0rSq87RL0uuSFkqaJ+nZNG9bSQ9J\nejn93KbqnDWSrpe0UtKi0ryGeVX4TWqTBZLGVpf8Y032Ybqk5akd5kmaVFr2w7QPSyQdWU3qj0na\nVdJsSYslvSjpvDQ/i3ZokT+LNpA0TNJcSfNT/p+k+SMlzUk5Z0oakuYPTfe70vIRVebvaznW4tzq\nMORfi12HO6INXIvNeisisrgBg4BXgFHAEGA+sHfVudrM/jqwfd28XwHT0vQ0YEbVOUvZDgbGAou6\nywtMAu4DBBwIzKk6f4t9mA5c2GDdvdPv01BgZPo9G1Rx/p2AsWl6C2BpyplFO7TIn0UbpOO4eZoe\nDMxJx/V2YGqafxVwVpo+G7gqTU8FZlZ5/Pv42GRZi3OrwylT1rXYdbgj2sC1uOI28C3fW07vJB0A\ndEXEqxHxX+A2YHLFmTbEZODGNH0jMKXCLOuJiCeAVXWzm+WdDNwUhb8BW0vaqX+SNtdkH5qZDNwW\nEasj4jWgi+L3rTIRsSIink/T7wEvAbuQSTu0yN9MR7VBOo7vp7uD0y2Aw4A70vz6419rlzuAr0hS\nP8XtbwOpFndsHYb8a7HrcEe0gWvxwK3F1sdy6iTtAvy9dH8Zrf/QO0kAD0p6TtLpad6OEbEiTf8D\n2LGaaG1rlje3djknDYO4vjS0pqP3IQ0X2I/iClp27VCXHzJpA0mDJM0DVgIPUVxRfTsi1qRVyhnX\n5U/L3wG269/E/abj2qpNA6EOQ4Y1oIEsakBZ7nUYXIvNeiqnTlLOJkTEWOAo4HuSDi4vjIigOIFn\nIbe8Jb8FdgPGACuAy6uN0z1JmwN/Bn4QEe+Wl+XQDg3yZ9MGEfFRRIwBhlNcSd2z4ki2YQZUHYY8\nM5NRDajJvQ6Da7FZb+TUSVoO7Fq6PzzN63gRsTz9XAn8heKP/M3a2/Dp58rqEralWd5s2iUi3kzF\ndi1wDR8PIejIfZA0mOKkdnNE3JlmZ9MOjfLn1gYAEfE2MBv4IsXwmU+lReWM6/Kn5VsB/+rnqP2l\nY9uqlQFShyGjGtBIbjUg9zoMrsUM3FpsfSynTtIzwB7pE02GUPxD3l0VZ+qWpM0kbVGbBo4AFlFk\nPzmtdjIwq5qEbWuW9y7gpPSpPgcC75SGIXSUurHhx1C0AxT7MDV9Ks5IYA9gbn/nK0tjqK8DXoqI\nX5cWZdEOzfLn0gaSPi1p6zS9KXA4xVj+2cCxabX6419rl2OBR9MV5oEou1o8gOowZFIDmsmlBkD+\ndRhcixnYtdj6Wv0nOXTyjeKTY5ZSjEe9uOo8bWYeRfFJMfOBF2u5KcbIPgK8DDwMbFt11lLmWyne\nfv+QYqzvqc3yUnzyzJWpTRYC46rO32If/pAyLqAopDuV1r847cMS4KgOyD+BYgjHAmBeuk3KpR1a\n5M+iDYB9gRdSzkXAJWn+KIoXDF3An4Chaf6wdL8rLR9V9e9QHx+frGpxjnU45cu6FrsOd0QbuBZX\n3Aa+5XtThDvYZmZmZmZmNTkNtzMzMzMzM+tz7iSZmZmZmZmVuJNkZmZmZmZW4k6SmZmZmZlZiTtJ\nZmZmZmZmJe4k2XokhaTLS/cvlDR9Iz327yUd2/2aG/w8x0l6SdLs0rzPSZqXbqskvZamH+7hYz9Q\n+76VFuv8TNKhvc1f91jLJC2UtEDS/ZJ22Aj5TpH0mY2Rz8z6hmtxt4/tWmxmfcqdJKu3GviGpO2r\nDlJW+mbtdpwKnBYR606OEbEwIsZExBiK74S4KN2f2JPniYgjI+K9bta5OCJmt1qnhw6KiH0pvidi\n2obmA04BfGI262yuxS24FptZX3MnyeqtAa4Gzq9fUH/1UdL76echkh6XNEvSq5J+KelESXPTlbfd\nSg8zUdKzkpZKOjptP0jSZZKeSVfpzig97pOS7gIWN8hzQnr8RZJmpHmXUHx53nWSLmtnhyVNlPSY\npHsovlwPSXdLek7Si5K+W1p3maStJe2enve6tM59koaldf4oaUpp/emSXkj7NjrN30HSI2nb30la\nXvtW8RaeAHZP23+rtO8/bzefpOOBMcDMdPV2SDr2i1O+Ge0cMzPrc67FuBabWXXcSbJGrgROlLRV\nD7b5PHAmsBfwbWB0RBwAXAucW1pvBHAA8DXgqnQyOxV4JyLGA+OB0ySNTOuPBc6LiNHlJ5O0MzAD\nOIziRDNe0pSIuBR4FjgxIi7qQf5xwNkRsVe6f3JE7J/yXCBpmwbbfBa4IiL2AT4ApjR57DcjYj+K\nY3FBmncpcH/a9m5g51bhJAk4GlgoaTjwU+BQYD/gS7UXOd3li4iZFN+4fny6krsNxbev75OukP6i\nVQ4z61euxa7FZlYRd5Ls/0TEu8BNwPd7sNkzEbEiIlYDrwAPpvkLKU7GNbdHxNqIeBl4FdgTOAI4\nSdI8YA6wHbBHWn9uRLzW4PnGA49FxFsRsQa4GTi4B3nrPR0Rb5Tuny9pPvA0MBzYrcE2XRGxME0/\nx/r7WXZng3UmALcBRMQ9QKthGU9SnEw3pXgx8gXg0Yj4Z0R8CNxC431vJ98qYC1wjaRjgH+3yGFm\n/ci1GHAtNrOK9GRssX2yXAE8D9xQmreG1LGWtAkwpLRsdWl6ben+Wtb/PYu65wlAwLkR8UB5gaRD\n6L8TxbrnkTSR4kR3YER8IOkpYFiDbcr7/BHN/55Wt7FOKwdFxNulfO1u122+iPhQ0jjgcOA44CyK\nF0pm1hlci12LzawCfifJGoqIVcDtFMMval4H9k/TXwcG9+Khj5O0SRobPwpYAjwAnCVpMICk0ZI2\n6+Zx5gJflrS9pEHACcDjvcjTyFbAqnRS3ofiSunG9lfgmwCSJgEtPwWpzhzgUEnbqfjn5qn0bN/f\nqz2fik9f2jJdQT2fYsiImXUI12LXYjOrht9JslYuB84p3b8GmJWGPtxP764svkFxUt2gEmvlAAAA\n6ElEQVQSODMi/iPpWorhB8+n8d5v0XxMOQARsULSNGA2xdXPeyNiVi/yNHIvcLqkxRQvHOZspMct\n+zFwi6TvAE8BK2nzeEbEMkk/Ah6j2Pe7I+LeHjz3DcC1kj6geIF1h6ShFBdNLmi5pZlVwbXYtdjM\n+pki6t9xN7O+lv5Jek1ErJE0geKfesdVncvM7JPEtdjMmvE7SWbVGAHcmoanrAbOqDaOmdkn0ghc\ni82sAb+TZGZmZmZmVuIPbjAzMzMzMytxJ8nMzMzMzKzEnSQzMzMzM7MSd5LMzMzMzMxK3EkyMzMz\nMzMr+R8kNV7lXJtW6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153f6908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据不同的训练集大小，和最大深度，生成学习曲线\n",
    "vs.ModelLearning(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 学习曲线\n",
    "*选择上述图像中的其中一个，并给出其最大深度。随着训练数据量的增加，训练集曲线的评分有怎样的变化？验证集曲线呢？如果有更多的训练数据，是否能有效提升模型的表现呢？*\n",
    "\n",
    "**提示：**学习曲线的评分是否最终会收敛到特定的值？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 回答:\n",
    "> * max_deth = 3 最好\n",
    "\n",
    "> * 随着训练数据量的增大，训练集评分不断下降，\n",
    "> * 随着训练数据量的增大，验证集评分不断增加\n",
    "\n",
    "> *  如果有更多的训练数据，模型会有提升，但是效果不明显"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 复杂度曲线\n",
    "下列代码内的区域会输出一幅图像，它展示了一个已经经过训练和验证的决策树模型在不同最大深度条件下的表现。这个图形将包含两条曲线，一个是训练集的变化，一个是验证集的变化。跟**学习曲线**相似，阴影区域代表该曲线的不确定性，模型训练和测试部分的评分都用的 `performance_metric` 函数。\n",
    "\n",
    "运行下方区域中的代码，并利用输出的图形并回答下面的两个问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rexdrW9aS582jMK9wDF+AUkOzu8+yvh2WBttnoO33dJ/dGcly724f94tH6rli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\nzxhjTFD5f/vwfxbVCb8gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153ff4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据不同的最大深度参数，生成复杂度曲线\n",
    "vs.ModelComplexity(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5 - 偏差（bias）与方差（variance）之间的权衡取舍\n",
    "*当模型以最大深度 1训练时，模型的预测是出现很大的偏差还是出现了很大的方差？当模型以最大深度10训练时，情形又如何呢？图形中的哪些特征能够支持你的结论？*\n",
    "  \n",
    "**提示：** 你如何得知模型是否出现了偏差很大或者方差很大的问题？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5 - 回答:\n",
    ">* 最大深度为1时，出现了很大的偏差 \n",
    "\n",
    ">* 最大深度为10时，出现的偏差和方差都很大，但是偏差优于最大深度为1，方差大于深度为1\n",
    "\n",
    ">* $R^2$值越小，偏差越大，std的范围越大，方差越大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6- 最优模型的猜测\n",
    "*结合问题 5 中的图，你认为最大深度是多少的模型能够最好地对未见过的数据进行预测？你得出这个答案的依据是什么？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6 - 回答:\n",
    ">* 最好的深度为 4 \n",
    ">* 当深度为4时候时，验证集偏差最小，方差较小。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 第五步. 选择最优参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7- 网格搜索（Grid Search）\n",
    "*什么是网格搜索法？如何用它来优化模型？*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7 - 回答:\n",
    ">* 网格搜索法是一种通过遍历给定的的参数组来优化模型的表现。\n",
    ">* 通过GridSearchCV穷举提供的网格搜索参数，根据参数不断生成组合，最后根据结果来进行判断选择那个参数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8 - 交叉验证\n",
    "- 什么是K折交叉验证法（k-fold cross-validation）？\n",
    "- [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)是如何结合交叉验证来完成对最佳参数组合的选择的？\n",
    "- [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)中的`'cv_results_'`属性能告诉我们什么？\n",
    "- 网格搜索时如果不使用交叉验证会有什么问题？交叉验证又是如何解决这个问题的？\n",
    "\n",
    "**提示：** 在下面 fit_model函数最后加入 `print pd.DataFrame(grid.cv_results_)` 可以帮你查看更多信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8 - 回答：\n",
    "\n",
    ">* K折交叉验证法就是将数据按照比例分成K份，每次取其中一份做测试，其他的做训练集，计算分数，将得到的所有分数做一个平均值，此平均值就是当前模型的分数，根据模型的分数来判断当前模型的好坏。\n",
    ">* GridSearchCV中有一个参数叫做cv,可以构建交叉验证对象然后传入。\n",
    ">* 平均值、标准差、交叉验证等一系列的分数和计算时间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 4：训练最优模型\n",
    "在这个练习中，你将需要将所学到的内容整合，使用**决策树算法**训练一个模型。为了得出的是一个最优模型，你需要使用网格搜索法训练模型，以找到最佳的 `'max_depth'` 参数。你可以把`'max_depth'` 参数理解为决策树算法在做出预测前，允许其对数据提出问题的数量。决策树是**监督学习算法**中的一种。\n",
    "\n",
    "在下方 `fit_model` 函数中，你需要做的是：\n",
    "1. **定义 `'cross_validator'` 变量**: 使用 `sklearn.model_selection` 中的 [`KFold`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html) 创建一个交叉验证生成器对象;\n",
    "2. **定义 `'regressor'` 变量**: 使用  `sklearn.tree` 中的 [`DecisionTreeRegressor`](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html) 创建一个决策树的回归函数;\n",
    "3. **定义 `'params'` 变量**: 为 `'max_depth'` 参数创造一个字典，它的值是从1至10的数组;\n",
    "4. **定义 `'scoring_fnc'` 变量**: 使用 `sklearn.metrics` 中的 [`make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html)  创建一个评分函数；\n",
    " 将 `‘performance_metric’` 作为参数传至这个函数中；\n",
    "5. **定义 `'grid'` 变量**: 使用 `sklearn.model_selection` 中的 [`GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) 创建一个网格搜索对象；将变量`'regressor'`, `'params'`, `'scoring_fnc'`和 `'cross_validator'` 作为参数传至这个对象构造函数中；\n",
    "  \n",
    "如果你对python函数的默认参数定义和传递不熟悉，可以参考这个MIT课程的[视频](http://cn-static.udacity.com/mlnd/videos/MIT600XXT114-V004200_DTH.mp4)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 4\n",
    "\n",
    "#提示: 导入 'KFold' 'DecisionTreeRegressor' 'make_scorer' 'GridSearchCV' \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import  KFold\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "def fit_model(X, y):\n",
    "    \"\"\" 基于输入数据 [X,y]，利于网格搜索找到最优的决策树模型\"\"\"\n",
    "    \n",
    "    cross_validator = KFold(n_splits=10)\n",
    "    \n",
    "    regressor = DecisionTreeRegressor()\n",
    "\n",
    "    params = {'max_depth':range(1,10)}\n",
    "\n",
    "    scoring_fnc = make_scorer(performance_metric)\n",
    "\n",
    "    grid = GridSearchCV(regressor,params,scoring_fnc,cv=cross_validator)\n",
    "\n",
    "    # 基于输入数据 [X,y]，进行网格搜索\n",
    "    grid = grid.fit(X, y)\n",
    "#     import pandas as pd\n",
    "#     print(pd.DataFrame(grid.cv_results_))\n",
    "    # 返回网格搜索后的最优模型\n",
    "    return grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 4：训练最优模型 （可选）\n",
    "在这个练习中，你将需要将所学到的内容整合，使用**决策树算法**训练一个模型。为了得出的是一个最优模型，你需要使用网格搜索法训练模型，以找到最佳的 `'max_depth'` 参数。你可以把`'max_depth'` 参数理解为决策树算法在做出预测前，允许其对数据提出问题的数量。决策树是**监督学习算法**中的一种。\n",
    "\n",
    "在下方 `fit_model` 函数中，你需要做的是：\n",
    "\n",
    "- 遍历参数`‘max_depth’`的可选值 1～10，构造对应模型\n",
    "- 计算当前模型的交叉验证分数\n",
    "- 返回最优交叉验证分数对应的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 4 可选\n",
    "\n",
    "'''\n",
    "不允许使用 DecisionTreeRegressor 以外的任何 sklearn 库\n",
    "\n",
    "提示: 你可能需要实现下面的 cross_val_score 函数\n",
    "\n",
    "def cross_val_score(estimator, X, y, scoring = performance_metric, cv=3):\n",
    "    \"\"\" 返回每组交叉验证的模型分数的数组 \"\"\"\n",
    "    scores = [0,0,0]\n",
    "    return scores\n",
    "'''\n",
    "\n",
    "def fit_model2(X, y):\n",
    "    \"\"\" 基于输入数据 [X,y]，利于网格搜索找到最优的决策树模型\"\"\"\n",
    "    \n",
    "    #最优交叉验证分数对应的最优模型\n",
    "    best_estimator = None\n",
    "    \n",
    "    return best_estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 9 - 最优模型\n",
    "*最优模型的最大深度（maximum depth）是多少？此答案与你在**问题 6**所做的猜测是否相同？*\n",
    "\n",
    "运行下方区域内的代码，将决策树回归函数代入训练数据的集合，以得到最优化的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'max_depth' is 4 for the optimal model.\n"
     ]
    }
   ],
   "source": [
    "# 基于训练数据，获得最优模型\n",
    "optimal_reg = fit_model(X_train, y_train)\n",
    "\n",
    "# 输出最优模型的 'max_depth' 参数\n",
    "print (\"Parameter 'max_depth' is {} for the optimal model.\".format(optimal_reg.get_params()['max_depth']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 9 - 回答：\n",
    ">* 最优模型的最大深度（maximum depth）是 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第六步. 做出预测\n",
    "当我们用数据训练出一个模型，它现在就可用于对新的数据进行预测。在决策树回归函数中，模型已经学会对新输入的数据*提问*，并返回对**目标变量**的预测值。你可以用这个预测来获取数据未知目标变量的信息，这些数据必须是不包含在训练数据之内的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 10 - 预测销售价格\n",
    "想像你是一个在波士顿地区的房屋经纪人，并期待使用此模型以帮助你的客户评估他们想出售的房屋。你已经从你的三个客户收集到以下的资讯:\n",
    "\n",
    "| 特征 | 客戶 1 | 客戶 2 | 客戶 3 |\n",
    "| :---: | :---: | :---: | :---: |\n",
    "| 房屋内房间总数 | 5 间房间 | 4 间房间 | 8 间房间 |\n",
    "| 社区贫困指数（％被认为是贫困阶层） | 17% | 32% | 3% |\n",
    "| 邻近学校的学生-老师比例 | 15：1 | 22：1 | 12：1 |\n",
    "\n",
    "*你会建议每位客户的房屋销售的价格为多少？从房屋特征的数值判断，这样的价格合理吗？为什么？* \n",
    "\n",
    "**提示：**用你在**分析数据**部分计算出来的统计信息来帮助你证明你的答案。\n",
    "\n",
    "运行下列的代码区域，使用你优化的模型来为每位客户的房屋价值做出预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted selling price for Client 1's home: $403,025.00\n",
      "Predicted selling price for Client 2's home: $237,478.72\n",
      "Predicted selling price for Client 3's home: $931,636.36\n"
     ]
    }
   ],
   "source": [
    "# 生成三个客户的数据\n",
    "client_data = [[5, 17, 15], # 客户 1\n",
    "               [4, 32, 22], # 客户 2\n",
    "               [8, 3, 12]]  # 客户 3\n",
    "\n",
    "# 进行预测\n",
    "predicted_price = optimal_reg.predict(client_data)\n",
    "for i, price in enumerate(predicted_price):\n",
    "    print (\"Predicted selling price for Client {}'s home: ${:,.2f}\".format(i+1, price))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 10 - 回答：\n",
    ">* Predicted selling price for Client 1's home: $403,025.00\n",
    "\n",
    ">* Predicted selling price for Client 2's home: $237,478.72\n",
    "\n",
    ">* Predicted selling price for Client 3's home: $931,636.36"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编程练习 5\n",
    "你刚刚预测了三个客户的房子的售价。在这个练习中，你将用你的最优模型在整个测试数据上进行预测, 并计算相对于目标变量的决定系数 R<sup>2</sup>的值**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal model has R^2 score 0.84 on test data\n"
     ]
    }
   ],
   "source": [
    "#TODO 5\n",
    "\n",
    "# 提示：你可能需要用到 X_test, y_test, optimal_reg, performance_metric\n",
    "# 提示：你可能需要参考问题10的代码进行预测\n",
    "# 提示：你可能需要参考问题3的代码来计算R^2的值\n",
    "predict_test = optimal_reg.predict(X_test)\n",
    "\n",
    "r2 = performance_metric(y_test,predict_test)\n",
    "\n",
    "print (\"Optimal model has R^2 score {:,.2f} on test data\".format(r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题11 - 分析决定系数\n",
    "\n",
    "你刚刚计算了最优模型在测试集上的决定系数，你会如何评价这个结果？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题11 - 回答\n",
    "\n",
    ">* model has $R^2$ score 0.84 on test data\n",
    ">* 效果一般"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型健壮性\n",
    "\n",
    "一个最优的模型不一定是一个健壮模型。有的时候模型会过于复杂或者过于简单，以致于难以泛化新增添的数据；有的时候模型采用的学习算法并不适用于特定的数据结构；有的时候样本本身可能有太多噪点或样本过少，使得模型无法准确地预测目标变量。这些情况下我们会说模型是欠拟合的。\n",
    "\n",
    "### 问题 12 - 模型健壮性\n",
    "\n",
    "模型是否足够健壮来保证预测的一致性？\n",
    "\n",
    "**提示**: 执行下方区域中的代码，采用不同的训练和测试集执行 `fit_model` 函数10次。注意观察对一个特定的客户来说，预测是如何随训练数据的变化而变化的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1: $391,183.33\n",
      "Trial 2: $411,417.39\n",
      "Trial 3: $415,800.00\n",
      "Trial 4: $420,622.22\n",
      "Trial 5: $423,300.00\n",
      "Trial 6: $411,931.58\n",
      "Trial 7: $399,663.16\n",
      "Trial 8: $407,232.00\n",
      "Trial 9: $402,531.82\n",
      "Trial 10: $413,700.00\n",
      "\n",
      "Range in prices: $32,116.67\n"
     ]
    }
   ],
   "source": [
    "# 请先注释掉 fit_model 函数里的所有 print 语句\n",
    "vs.PredictTrials(features, prices, fit_model, client_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 12 - 回答：\n",
    ">* 模型不够足够健壮性保持预测的一致性\n",
    ">* 使用不同的训练和测试集的时候，数据波动范围不小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 13 - 实用性探讨\n",
    "*简单地讨论一下你建构的模型能否在现实世界中使用？* \n",
    "\n",
    "提示：回答以下几个问题，并给出相应结论的理由：\n",
    "- *1978年所采集的数据，在已考虑通货膨胀的前提下，在今天是否仍然适用？*\n",
    "- *数据中呈现的特征是否足够描述一个房屋？*\n",
    "- *在波士顿这样的大都市采集的数据，能否应用在其它乡镇地区？*\n",
    "- *你觉得仅仅凭房屋所在社区的环境来判断房屋价值合理吗？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 13 - 回答：\n",
    "\n",
    ">* bu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可选问题 - 预测北京房价\n",
    "\n",
    "（本题结果不影响项目是否通过）通过上面的实践，相信你对机器学习的一些常用概念有了很好的领悟和掌握。但利用70年代的波士顿房价数据进行建模的确对我们来说意义不是太大。现在你可以把你上面所学应用到北京房价数据集中 `bj_housing.csv`。\n",
    "\n",
    "免责声明：考虑到北京房价受到宏观经济、政策调整等众多因素的直接影响，预测结果仅供参考。\n",
    "\n",
    "这个数据集的特征有：\n",
    "- Area：房屋面积，平方米\n",
    "- Room：房间数，间\n",
    "- Living: 厅数，间\n",
    "- School: 是否为学区房，0或1\n",
    "- Year: 房屋建造时间，年\n",
    "- Floor: 房屋所处楼层，层\n",
    "\n",
    "目标变量：\n",
    "- Value: 房屋人民币售价，万\n",
    "\n",
    "你可以参考上面学到的内容，拿这个数据集来练习数据分割与重排、定义衡量标准、训练模型、评价模型表现、使用网格搜索配合交叉验证对参数进行调优并选出最佳参数，比较两者的差别，最终得出最佳模型对验证集的预测分数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 6\n",
    "\n",
    "# 你的代码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题14 - 北京房价预测\n",
    "你成功的用新的数据集构建了模型了吗？他能对测试数据进行验证吗？它的表现是否符合你的预期？交叉验证是否有助于提升你模型的表现？\n",
    "\n",
    "**提示：**如果你是从零开始构建机器学习的代码会让你一时觉得无从下手。这时不要着急，你要做的只是查看之前写的代码，把每一行都看明白，然后逐步构建你的模型。当中遇到什么问题也可以在我们论坛寻找答案。也许你会发现你所构建的模型的表现并没有达到你的预期，这说明机器学习并非是一项简单的任务，构建一个表现良好的模型需要长时间的研究和测试。这也是我们接下来的课程中会逐渐学到的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题14 - 回答"
   ]
  }
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